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ae4a0fc5-bb0d-4b73-8539-f8e0f17c7a01 | structure-determination | 2108.02706 | null | https://arxiv.org/abs/2108.02706v1 | https://arxiv.org/pdf/2108.02706v1.pdf | Structure determination | While many good textbooks are available on Protein Structure, Molecular Simulations, Thermodynamics and Bioinformatics methods in general, there is no good introductory level book for the field of Structural Bioinformatics. This book aims to give an introduction into Structural Bioinformatics, which is where the previo... | ['K. Anton Feenstra', 'Sanne Abeln', 'Katharina Waury', 'Jose Gavaldá-García', 'Hugo van Ingen', 'Bas Stringer', 'Halima Mouhib'] | 2021-08-05 | null | null | null | null | ['cryogenic-electron-microscopy-cryo-em'] | ['computer-vision'] | [ 3.50267559e-01 -1.72770277e-01 -1.81482002e-01 -1.51812598e-01
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916d311c-fecb-4d46-88ed-1d0a36080557 | slav-ner-the-3rd-cross-lingual-challenge-on | null | null | https://aclanthology.org/2021.bsnlp-1.15 | https://aclanthology.org/2021.bsnlp-1.15.pdf | Slav-NER: the 3rd Cross-lingual Challenge on Recognition, Normalization, Classification, and Linking of Named Entities across Slavic Languages | This paper describes Slav-NER: the 3rd Multilingual Named Entity Challenge in Slavic languages. The tasks involve recognizing mentions of named entities in Web documents, normalization of the names, and cross-lingual linking. The Challenge covers six languages and five entity types, and is organized as part of the 8th ... | ['Roman Yangarber', 'Josef Steinberger', 'Vasyl Starko', 'Marko Robnik-Sikonja', 'Ivaylo Radev', 'Pavel Přibáň', 'Senja Pollak', 'Lidia Pivovarova', 'Petya Osenova', 'Preslav Nakov', 'Michał Marcińczuk', 'Maria Lebedeva', 'Olga Kanishcheva', 'Zara Kancheva', 'Bogdan Babych', 'Jakub Piskorski'] | null | null | null | null | eacl-bsnlp-2021-4 | ['cross-lingual-entity-linking'] | ['natural-language-processing'] | [-5.45303762e-01 7.31322393e-02 -5.04935801e-01 -4.04019207e-01
-1.11319900e+00 -1.22088373e+00 7.64418364e-01 5.04593968e-01
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0ef34d77-9bcd-41ed-9508-a95d46889d23 | exponential-concentration-for-mutual | null | null | http://papers.nips.cc/paper/4768-exponential-concentration-for-mutual-information-estimation-with-application-to-forests | http://papers.nips.cc/paper/4768-exponential-concentration-for-mutual-information-estimation-with-application-to-forests.pdf | Exponential Concentration for Mutual Information Estimation with Application to Forests | We prove a new exponential concentration inequality for a plug-in estimator of the Shannon mutual information. Previous results on mutual information estimation only bounded expected error. The advantage of having the exponential inequality is that, combined with the union bound, we can guarantee accurate estimators of... | ['John D. Lafferty', 'Han Liu', 'Larry Wasserman'] | 2012-12-01 | null | null | null | neurips-2012-12 | ['mutual-information-estimation'] | ['methodology'] | [ 2.45339379e-01 3.97111326e-01 -2.40799680e-01 -2.82620519e-01
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-7.77519286e-01 5.77587426e-01 4.73224781e-02 3.07676315... | [7.261630535125732, 4.630176067352295] |
c8d2f1f2-61f0-46ca-b461-0b587b56b17e | nestedformer-nested-modality-aware | 2208.14876 | null | https://arxiv.org/abs/2208.14876v1 | https://arxiv.org/pdf/2208.14876v1.pdf | NestedFormer: Nested Modality-Aware Transformer for Brain Tumor Segmentation | Multi-modal MR imaging is routinely used in clinical practice to diagnose and investigate brain tumors by providing rich complementary information. Previous multi-modal MRI segmentation methods usually perform modal fusion by concatenating multi-modal MRIs at an early/middle stage of the network, which hardly explores ... | ['Lei Zhu', 'Tong Han', 'Liang Wan', 'Lequan Yu', 'Zhaohu Xing'] | 2022-08-31 | null | null | null | null | ['brain-tumor-segmentation'] | ['medical'] | [ 3.31582397e-01 2.20716447e-02 -1.31204173e-01 -5.04000783e-01
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-6.72253743e-02 3.61214817e-01 5.28899550e-01 -1.67677358... | [14.55521011352539, -2.3389477729797363] |
c4610a86-41db-4d8c-ad30-36fb362e2d33 | cbnetv2-a-composite-backbone-network | 2107.00420 | null | https://arxiv.org/abs/2107.00420v7 | https://arxiv.org/pdf/2107.00420v7.pdf | CBNet: A Composite Backbone Network Architecture for Object Detection | Modern top-performing object detectors depend heavily on backbone networks, whose advances bring consistent performance gains through exploring more effective network structures. In this paper, we propose a novel and flexible backbone framework, namely CBNetV2, to construct high-performance detectors using existing ope... | ['Haibin Ling', 'Jingdong Chen', 'Wei Chu', 'Zhi Tang', 'Yongtao Wang', 'Yudong Liu', 'Xiaojie Chu', 'TingTing Liang'] | 2021-07-01 | null | null | null | null | ['real-time-object-detection'] | ['computer-vision'] | [-1.42396510e-01 1.25173837e-01 -3.18554133e-01 -2.70509690e-01
-4.24068391e-01 -4.53958124e-01 3.43614340e-01 -2.02890351e-01
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6.29134402e-02 1.61529660e-01 9.96699691e-01 -2.71253854... | [8.821584701538086, 0.015470059588551521] |
a84b1e1a-f1dc-4802-8f31-8b2232f0ace3 | bias-reduced-multi-step-hindsight-experience | 2102.12962 | null | https://arxiv.org/abs/2102.12962v3 | https://arxiv.org/pdf/2102.12962v3.pdf | Bias-reduced Multi-step Hindsight Experience Replay for Efficient Multi-goal Reinforcement Learning | Multi-goal reinforcement learning is widely applied in planning and robot manipulation. Two main challenges in multi-goal reinforcement learning are sparse rewards and sample inefficiency. Hindsight Experience Replay (HER) aims to tackle the two challenges via goal relabeling. However, HER-related works still need mill... | ['Jiangpeng Yan', 'Xiu Li', 'Lanqing Li', 'Dijun Luo', 'Feng Luo', 'Yu Yang', 'Jiafei Lyu', 'Rui Yang'] | 2021-02-25 | null | null | null | null | ['multi-goal-reinforcement-learning'] | ['methodology'] | [-1.03258535e-01 1.59111589e-01 -4.32868183e-01 -2.06559435e-01
-1.06863236e+00 -1.15723945e-01 1.11105055e-01 -1.06212627e-02
-9.71684515e-01 1.14116669e+00 1.10270038e-01 -2.26386532e-01
-4.28754717e-01 -7.53296912e-01 -8.20440888e-01 -6.46852076e-01
-4.55024391e-01 3.99595380e-01 1.10412315e-02 -7.85898268... | [4.133662700653076, 1.7140182256698608] |
e32d38a8-0d40-4293-a43a-a9d70bbd8931 | from-temporal-to-contemporaneous-iterative | 2306.00624 | null | https://arxiv.org/abs/2306.00624v1 | https://arxiv.org/pdf/2306.00624v1.pdf | From Temporal to Contemporaneous Iterative Causal Discovery in the Presence of Latent Confounders | We present a constraint-based algorithm for learning causal structures from observational time-series data, in the presence of latent confounders. We assume a discrete-time, stationary structural vector autoregressive process, with both temporal and contemporaneous causal relations. One may ask if temporal and contempo... | ['Gal Novik', 'Yaniv Gurwicz', 'Shami Nisimov', 'Raanan Y. Rohekar'] | 2023-06-01 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 4.44250613e-01 2.55907893e-01 -4.28309351e-01 -3.83571863e-01
-4.17421788e-01 -4.85815108e-01 1.06764328e+00 4.52895164e-01
-1.76352695e-01 1.16271424e+00 6.06681585e-01 -7.19258487e-01
-9.86948609e-01 -8.22876573e-01 -8.57084394e-01 -5.37885189e-01
-1.01135767e+00 7.56043196e-01 2.22827032e-01 1.34630606... | [7.728259563446045, 5.14849328994751] |
3ac611d0-a870-43a9-a881-f892f6eeca68 | adaptive-wing-loss-for-robust-face-alignment | 1904.07399 | null | https://arxiv.org/abs/1904.07399v3 | https://arxiv.org/pdf/1904.07399v3.pdf | Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression | Heatmap regression with a deep network has become one of the mainstream approaches to localize facial landmarks. However, the loss function for heatmap regression is rarely studied. In this paper, we analyze the ideal loss function properties for heatmap regression in face alignment problems. Then we propose a novel lo... | ['Li Fuxin', 'Xinyao Wang', 'Liefeng Bo'] | 2019-04-16 | adaptive-wing-loss-for-robust-face-alignment-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Wang_Adaptive_Wing_Loss_for_Robust_Face_Alignment_via_Heatmap_Regression_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Adaptive_Wing_Loss_for_Robust_Face_Alignment_via_Heatmap_Regression_ICCV_2019_paper.pdf | iccv-2019-10 | ['robust-face-alignment'] | ['computer-vision'] | [-2.28794396e-01 1.04190670e-01 -3.71946067e-01 -7.36093462e-01
-6.38256371e-01 -4.78502065e-02 2.59752154e-01 -2.12249175e-01
-2.56111115e-01 5.92056453e-01 7.49432892e-02 1.40653595e-01
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1.40975654e-01 5.01681089e-01 1.64126605e-01 -2.32031159... | [13.462800025939941, 0.4315337538719177] |
1ebb27bb-5022-4eee-89db-cc43d64fd6b3 | benchmark-for-anonymous-video-analytics | 2009.14684 | null | https://arxiv.org/abs/2009.14684v3 | https://arxiv.org/pdf/2009.14684v3.pdf | Benchmark for Anonymous Video Analytics | Out-of-home audience measurement aims to count and characterize the people exposed to advertising content in the physical world. While audience measurement solutions based on computer vision are of increasing interest, no commonly accepted benchmark exists to evaluate and compare their performance. In this paper, we pr... | ['Ricardo Sanchez-Matilla', 'Andrea Cavallaro'] | 2020-09-30 | null | null | null | null | ['age-and-gender-estimation'] | ['computer-vision'] | [-1.39001936e-01 -4.66006905e-01 -1.45769492e-01 -4.27335769e-01
-1.22300541e+00 -7.76177227e-01 7.63372898e-01 5.56495309e-01
-5.76951861e-01 4.31164950e-01 2.54965842e-01 -2.21628040e-01
1.60599530e-01 -9.23299432e-01 -3.68251681e-01 -3.45549822e-01
-1.51766266e-03 8.14131618e-01 2.74515480e-01 9.13715586... | [13.353639602661133, 1.16133713722229] |
bf11b663-27e7-4112-adec-ad898f0f003f | the-comma-dataset-v0-2-annotating-aggression | 2111.10390 | null | https://arxiv.org/abs/2111.10390v1 | https://arxiv.org/pdf/2111.10390v1.pdf | The ComMA Dataset V0.2: Annotating Aggression and Bias in Multilingual Social Media Discourse | In this paper, we discuss the development of a multilingual dataset annotated with a hierarchical, fine-grained tagset marking different types of aggression and the "context" in which they occur. The context, here, is defined by the conversational thread in which a specific comment occurs and also the "type" of discurs... | ['Yogesh Dawer', 'Akash Bhagat', 'Siddharth Singh', 'Shyam Ratan', 'Laishram Niranjana Devi', 'Enakshi Nandi', 'Ritesh Kumar'] | 2021-11-19 | null | https://aclanthology.org/2022.lrec-1.441 | https://aclanthology.org/2022.lrec-1.441.pdf | lrec-2022-6 | ['aggression-identification'] | ['natural-language-processing'] | [-5.82605600e-01 -4.12632748e-02 5.60624488e-02 -4.48797494e-01
-2.46732369e-01 -6.68232501e-01 8.58915567e-01 6.51532114e-01
-8.83746266e-01 9.69920576e-01 1.09149671e+00 -1.08784959e-01
-9.65157002e-02 -4.59994793e-01 2.82714456e-01 -5.93085647e-01
1.40075356e-01 8.48632634e-01 2.58331180e-01 -7.41784811... | [8.828258514404297, 10.576807022094727] |
f335caec-2b20-47e5-85e5-d770a2a8ec7a | adversarial-robustness-with-non-uniform | 2102.12002 | null | https://arxiv.org/abs/2102.12002v4 | https://arxiv.org/pdf/2102.12002v4.pdf | Adversarial Robustness with Non-uniform Perturbations | Robustness of machine learning models is critical for security related applications, where real-world adversaries are uniquely focused on evading neural network based detectors. Prior work mainly focus on crafting adversarial examples (AEs) with small uniform norm-bounded perturbations across features to maintain the r... | ['Sergul Aydore', 'Luca Melis', 'Jeffrey Bickford', 'Ecenaz Erdemir'] | 2021-02-24 | null | http://proceedings.neurips.cc/paper/2021/hash/9fd98f856d3ca2086168f264a117ed7c-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/9fd98f856d3ca2086168f264a117ed7c-Paper.pdf | neurips-2021-12 | ['spam-detection'] | ['natural-language-processing'] | [ 3.50202113e-01 -1.21867009e-01 -9.41936597e-02 -1.22261502e-01
-5.65191686e-01 -1.25272357e+00 6.39896154e-01 3.46340984e-01
-3.60506564e-01 5.39377332e-01 -1.98206380e-01 -6.01197660e-01
-1.07192352e-01 -8.40955198e-01 -1.02522922e+00 -6.80223525e-01
-4.86573994e-01 -5.79424202e-02 2.34136999e-01 -3.74876946... | [5.730443954467773, 7.689039707183838] |
8a644c08-018a-484a-9b90-73fdec5fa74a | equivalent-classification-mapping-for-weakly | 2008.07728 | null | https://arxiv.org/abs/2008.07728v2 | https://arxiv.org/pdf/2008.07728v2.pdf | Equivalent Classification Mapping for Weakly Supervised Temporal Action Localization | Weakly supervised temporal action localization is a newly emerging yet widely studied topic in recent years. The existing methods can be categorized into two localization-by-classification pipelines, i.e., the pre-classification pipeline and the post-classification pipeline. The pre-classification pipeline first perfor... | ['Dingwen Zhang', 'Le Yang', 'Junwei Han', 'Tao Zhao'] | 2020-08-18 | null | null | null | null | ['weakly-supervised-temporal-action'] | ['computer-vision'] | [ 3.21295500e-01 -4.13686991e-01 -5.18261015e-01 -4.85878557e-01
-4.68263477e-01 -2.50316441e-01 5.06561458e-01 1.92784399e-01
-3.75164837e-01 2.88526326e-01 1.27296418e-01 1.16894081e-01
-3.57659608e-02 -7.72807658e-01 -3.94622207e-01 -8.22155952e-01
-6.38552010e-02 -1.08306073e-01 9.28091884e-01 6.97188601... | [8.569334030151367, 0.729658305644989] |
32e5dea5-4a35-4b51-bc4f-63e612a6c033 | joint-learning-of-salient-object-detection | 2203.04895 | null | https://arxiv.org/abs/2203.04895v2 | https://arxiv.org/pdf/2203.04895v2.pdf | Joint Learning of Salient Object Detection, Depth Estimation and Contour Extraction | Benefiting from color independence, illumination invariance and location discrimination attributed by the depth map, it can provide important supplemental information for extracting salient objects in complex environments. However, high-quality depth sensors are expensive and can not be widely applied. While general de... | ['Huchuan Lu', 'Lihe Zhang', 'Youwei Pang', 'Xiaoqi Zhao'] | 2022-03-09 | null | null | null | null | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [ 1.04817376e-01 -1.80394471e-01 -2.35347703e-01 -3.96697909e-01
-7.64682591e-01 -2.34098002e-01 3.19359511e-01 -7.74180591e-02
-2.25406229e-01 3.92720103e-01 2.41655171e-01 1.12277098e-01
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4.77042049e-01 -1.42122880e-01 7.38253653e-01 -2.17061058... | [9.65636157989502, -0.8891383409500122] |
cad799da-8b90-4956-9030-92f47c8cf9be | double-pessimism-is-provably-efficient-for | 2305.09659 | null | https://arxiv.org/abs/2305.09659v1 | https://arxiv.org/pdf/2305.09659v1.pdf | Double Pessimism is Provably Efficient for Distributionally Robust Offline Reinforcement Learning: Generic Algorithm and Robust Partial Coverage | We study distributionally robust offline reinforcement learning (robust offline RL), which seeks to find an optimal robust policy purely from an offline dataset that can perform well in perturbed environments. We propose a generic algorithm framework \underline{D}oubly \underline{P}essimistic \underline{M}odel-based \u... | ['Han Zhong', 'Tong Zhang', 'Miao Lu', 'Jose Blanchet'] | 2023-05-16 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [-2.88934261e-02 3.85673016e-01 -5.38427591e-01 1.38020784e-01
-1.46868396e+00 -9.05386448e-01 1.28431633e-01 9.85085219e-02
-5.35857499e-01 1.22076559e+00 -3.00441146e-01 -7.65965521e-01
-8.92135680e-01 -5.50270259e-01 -1.14172602e+00 -1.05495179e+00
-4.18252409e-01 6.31043077e-01 -1.23008564e-01 -1.05046064... | [4.371397972106934, 2.8594093322753906] |
426e4c3f-e667-4b0d-979a-560606e5dd3f | generative-mask-pyramid-network-forctcbct | 1907.00294 | null | https://arxiv.org/abs/1907.00294v4 | https://arxiv.org/pdf/1907.00294v4.pdf | Generative Mask Pyramid Network for CT/CBCT Metal Artifact Reduction with Joint Projection-Sinogram Correction | A conventional approach to computed tomography (CT) or cone beam CT (CBCT) metal artifact reduction is to replace the X-ray projection data within the metal trace with synthesized data. However, existing projection or sinogram completion methods cannot always produce anatomically consistent information to fill the meta... | ['S. Kevin Zhou', 'Wei-An Lin', 'Zhimin Huo', 'Jiebo Luo', 'William J. Sehnert', 'Levon Vogelsang', 'Haofu Liao'] | 2019-06-29 | null | null | null | null | ['metal-artifact-reduction'] | ['medical'] | [ 5.39805889e-01 1.29307047e-01 2.25621909e-01 -1.98285654e-01
-9.93495941e-01 -2.90887296e-01 7.88550302e-02 -2.57265508e-01
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4.49193925e-01 4.89203423e-01 5.96702993e-01 1.38454691... | [13.508596420288086, -2.542654275894165] |
d7aa411d-c43c-4666-9d55-9c56db67fa5b | smiss-a-protein-function-prediction-server-by | 1607.01384 | null | http://arxiv.org/abs/1607.01384v1 | http://arxiv.org/pdf/1607.01384v1.pdf | SMISS: A protein function prediction server by integrating multiple sources | SMISS is a novel web server for protein function prediction. Three different
predictors can be selected for different usage. It integrates different sources
to improve the protein function prediction accuracy, including the query
protein sequence, protein-protein interaction network, gene-gene interaction
network, and ... | [] | 2016-03-22 | null | null | null | null | ['protein-function-prediction'] | ['medical'] | [-1.98630039e-02 -3.01891714e-01 -3.33951533e-01 -4.68014240e-01
-3.90380323e-01 -8.13951731e-01 -2.91801453e-01 1.48975283e-01
-1.51617944e-01 1.39324498e+00 -3.20085615e-01 -7.96108484e-01
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1.91974357e-01 5.42415321e-01 9.97116625e-01 -2.50509053... | [4.757623672485352, 5.423917293548584] |
a057bf8f-6a3f-4191-9c93-d8cd5fd2981e | representation-and-bias-in-multilingual-nlp | null | null | https://openreview.net/forum?id=dKwmCtp6YI | https://openreview.net/pdf?id=dKwmCtp6YI | Representation and Bias in Multilingual NLP: Insights from Controlled Experiments on Conditional Language Modeling | Inspired by the phenomenon of performance disparity between languages in machine translation, we investigate whether and to what extent languages are equally hard to "conditional-language-model". Our goal is to improve our understanding and expectation of the relationship between language, data representation, size, an... | ['Ada Wan'] | 2021-01-01 | null | null | null | null | ['multilingual-nlp'] | ['natural-language-processing'] | [ 1.59418210e-01 -3.01675737e-01 -4.63856965e-01 -3.47900480e-01
-8.14655721e-01 -8.27254713e-01 7.62366593e-01 3.80615979e-01
-7.36489773e-01 6.39686167e-01 3.95548224e-01 -1.12547517e+00
6.57162890e-02 -4.73111302e-01 -5.82152426e-01 -5.25536954e-01
4.65036742e-02 5.23472071e-01 -1.35544822e-01 -3.66496831... | [11.159189224243164, 9.865483283996582] |
35c98d5e-d0ec-459c-b93d-0fda660fab96 | end-to-end-illuminant-estimation-based-on | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Xu_End-to-End_Illuminant_Estimation_Based_on_Deep_Metric_Learning_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Xu_End-to-End_Illuminant_Estimation_Based_on_Deep_Metric_Learning_CVPR_2020_paper.pdf | End-to-End Illuminant Estimation Based on Deep Metric Learning | Previous deep learning approaches to color constancy usually directly estimate illuminant value from input image. Such approaches might suffer heavily from being sensitive to the variation of image content. To overcome this problem, we introduce a deep metric learning approach named Illuminant-Guided Triplet Network (I... | [' Guoping Qiu', ' Bozhi Liu', ' Xianxu Hou', ' Jingxin Liu', 'Bolei Xu'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['color-constancy'] | ['computer-vision'] | [ 7.72272423e-02 -9.03211832e-01 1.35481313e-01 -6.22165859e-01
-4.78707850e-01 -7.70353138e-01 3.35905850e-01 -8.29896480e-02
-3.79541397e-01 4.53180701e-01 -1.11275978e-01 2.09102668e-02
1.32230118e-01 -7.55374849e-01 -7.54454613e-01 -8.66723359e-01
3.57528865e-01 -1.29338145e-01 -3.56869586e-02 6.16368726... | [10.452503204345703, -2.5587239265441895] |
4e9e67b8-842f-40dd-9622-5b584ff92f6a | neural-guided-ransac-learning-where-to-sample | 1905.04132 | null | https://arxiv.org/abs/1905.04132v2 | https://arxiv.org/pdf/1905.04132v2.pdf | Neural-Guided RANSAC: Learning Where to Sample Model Hypotheses | We present Neural-Guided RANSAC (NG-RANSAC), an extension to the classic RANSAC algorithm from robust optimization. NG-RANSAC uses prior information to improve model hypothesis search, increasing the chance of finding outlier-free minimal sets. Previous works use heuristic side-information like hand-crafted descriptor ... | ['Carsten Rother', 'Eric Brachmann'] | 2019-05-10 | neural-guided-ransac-learning-where-to-sample-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Brachmann_Neural-Guided_RANSAC_Learning_Where_to_Sample_Model_Hypotheses_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Brachmann_Neural-Guided_RANSAC_Learning_Where_to_Sample_Model_Hypotheses_ICCV_2019_paper.pdf | iccv-2019-10 | ['camera-localization', 'horizon-line-estimation'] | ['computer-vision', 'computer-vision'] | [-5.50742820e-02 -1.44119754e-01 -2.25048602e-01 -5.90935826e-01
-9.10810828e-01 -6.70539081e-01 6.78533554e-01 -7.00102672e-02
-5.47213733e-01 5.21825194e-01 2.39331201e-01 -1.17358416e-02
-3.04674566e-01 -3.20744395e-01 -1.00746739e+00 -6.57147944e-01
8.92704651e-02 6.50445580e-01 6.03360459e-02 -3.63809019... | [7.880390644073486, -2.178342580795288] |
76edc26f-b677-43b8-afaa-daf7b4b3f55c | online-binaural-speech-separation-of-moving | 2303.07458 | null | https://arxiv.org/abs/2303.07458v1 | https://arxiv.org/pdf/2303.07458v1.pdf | Online Binaural Speech Separation of Moving Speakers With a Wavesplit Network | Binaural speech separation in real-world scenarios often involves moving speakers. Most current speech separation methods use utterance-level permutation invariant training (u-PIT) for training. In inference time, however, the order of outputs can be inconsistent over time particularly in long-form speech separation. T... | ['Nima Mesgarani', 'Cong Han'] | 2023-03-13 | null | null | null | null | ['online-clustering', 'speech-separation', 'speaker-separation'] | ['computer-vision', 'speech', 'speech'] | [-1.07212632e-03 -5.06970048e-01 3.57005119e-01 -2.45536596e-01
-1.36574471e+00 -7.26689935e-01 2.28877276e-01 -8.87802020e-02
-1.64418831e-01 4.22733188e-01 2.99180329e-01 -2.53675461e-01
-4.65449810e-01 1.22182839e-01 -5.77693403e-01 -1.14963603e+00
-3.15859586e-01 3.07928443e-01 1.83180451e-01 -6.55188691... | [14.858915328979492, 5.846365928649902] |
65d8cbb0-43d6-4e13-b7a9-ac7d1fb740c9 | networked-signal-and-information-processing | 2210.13767 | null | https://arxiv.org/abs/2210.13767v2 | https://arxiv.org/pdf/2210.13767v2.pdf | Networked Signal and Information Processing | The article reviews significant advances in networked signal and information processing, which have enabled in the last 25 years extending decision making and inference, optimization, control, and learning to the increasingly ubiquitous environments of distributed agents. As these interacting agents cooperate, new coll... | ['José M. F. Moura', 'Ali H. Sayed', 'Soummya Kar', 'Stefan Vlaski'] | 2022-10-25 | null | null | null | null | ['inference-optimization'] | ['audio'] | [-1.75149858e-01 -3.48225534e-02 -4.73938137e-02 -8.01219642e-02
-2.49217257e-01 -6.21465206e-01 7.13956535e-01 1.97432712e-01
-2.64298081e-01 7.95852721e-01 2.67178386e-01 7.11246282e-02
-3.69536728e-01 -7.16021001e-01 -9.62267257e-03 -7.40642965e-01
-7.43084013e-01 1.19157128e-01 -1.56090930e-01 -1.97617803... | [4.580641269683838, 2.82226300239563] |
113a0c26-ac2b-4db6-87ab-d6242a8a150d | rvmde-radar-validated-monocular-depth | 2109.05265 | null | https://arxiv.org/abs/2109.05265v3 | https://arxiv.org/pdf/2109.05265v3.pdf | RVMDE: Radar Validated Monocular Depth Estimation for Robotics | Stereoscopy exposits a natural perception of distance in a scene, and its manifestation in 3D world understanding is an intuitive phenomenon. However, an innate rigid calibration of binocular vision sensors is crucial for accurate depth estimation. Alternatively, a monocular camera alleviates the limitation at the expe... | ['Moongu Jeon', 'Muhammad Aasim Rafique', 'Muhamamd Ishfaq Hussain'] | 2021-09-11 | null | null | null | null | ['stereo-depth-estimation'] | ['computer-vision'] | [ 2.32231572e-01 -3.29807103e-01 3.76934595e-02 -5.44552684e-01
-4.79726523e-01 -3.94556016e-01 5.37514627e-01 -2.77067870e-01
-3.45886558e-01 6.07981265e-01 6.60038739e-02 2.42975373e-02
-1.50102489e-02 -1.10167360e+00 -7.04873979e-01 -8.99726689e-01
2.21801400e-01 6.08010180e-02 3.36400330e-01 -2.04672590... | [8.376413345336914, -2.3795254230499268] |
f4a48471-69ef-41e6-920d-f4249c0a1f1a | neural-granular-sound-synthesis | 2008.01393 | null | https://arxiv.org/abs/2008.01393v3 | https://arxiv.org/pdf/2008.01393v3.pdf | Neural Granular Sound Synthesis | Granular sound synthesis is a popular audio generation technique based on rearranging sequences of small waveform windows. In order to control the synthesis, all grains in a given corpus are analyzed through a set of acoustic descriptors. This provides a representation reflecting some form of local similarities across ... | ['Adrien Bitton', 'Tatsuya Harada', 'Philippe Esling'] | 2020-08-04 | null | null | null | null | ['audio-generation'] | ['audio'] | [ 3.83103609e-01 1.34353250e-01 1.86037943e-01 3.49089950e-02
-9.97386277e-01 -7.68853545e-01 7.18189001e-01 2.42275558e-03
-1.18172318e-01 6.14712119e-01 2.57129222e-01 2.07080007e-01
-1.32684633e-01 -1.16979408e+00 -7.48262107e-01 -1.07607234e+00
-9.36843306e-02 6.68433368e-01 5.41884184e-01 -4.00576234... | [15.62775993347168, 5.821403503417969] |
8cd1d030-7655-4efe-be25-2cc56e902af6 | the-liver-tumor-segmentation-benchmark-lits | 1901.04056 | null | https://arxiv.org/abs/1901.04056v2 | https://arxiv.org/pdf/1901.04056v2.pdf | The Liver Tumor Segmentation Benchmark (LiTS) | In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 20... | ['Bjoern Menze', 'Christopher Pal', 'Spyridon Bakas', 'Jorge Cardoso', 'Liping Zhang', 'Miao Yu', 'Yading Yuan', 'Simon Chun-Ho Yu', 'Xiaoping Yang', 'Daguang Xu', 'Jianrong Wu', 'Leon Weninger', 'Chunliang Wang', 'Christian Wachinger', 'Jordi Torres', 'Zengming Shen', 'Ignacio Sarasua', 'Oliver Rippel', 'Jordi Pont-Tu... | 2019-01-13 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [-3.20153236e-01 -4.82427031e-02 -3.49774480e-01 -3.31907123e-02
-9.96173024e-01 -5.81427038e-01 6.23695552e-01 4.13680941e-01
-1.76511556e-01 4.19175267e-01 4.42876756e-01 -5.54856360e-01
8.82594753e-03 -4.56416011e-01 -1.65886909e-01 -9.91132259e-01
-4.88002479e-01 6.33231759e-01 7.07593337e-02 5.62712371... | [14.5130033493042, -2.6671860218048096] |
e352a141-9b8e-4a3b-8770-d5434bb6f467 | findings-of-the-second-shared-task-on | 1710.07177 | null | http://arxiv.org/abs/1710.07177v1 | http://arxiv.org/pdf/1710.07177v1.pdf | Findings of the Second Shared Task on Multimodal Machine Translation and Multilingual Image Description | We present the results from the second shared task on multimodal machine
translation and multilingual image description. Nine teams submitted 19 systems
to two tasks. The multimodal translation task, in which the source sentence is
supplemented by an image, was extended with a new language (French) and two new
test set... | ['Loïc Barrault', 'Desmond Elliott', 'Lucia Specia', 'Stella Frank', 'Fethi Bougares'] | 2017-10-19 | findings-of-the-second-shared-task-on-1 | https://aclanthology.org/W17-4718 | https://aclanthology.org/W17-4718.pdf | ws-2017-9 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 4.60033566e-01 -1.17878526e-01 -8.51311162e-02 -4.76440132e-01
-1.51491249e+00 -1.14368582e+00 1.02797306e+00 -1.25065953e-01
-9.39344823e-01 1.21962214e+00 3.49155776e-02 -2.49359369e-01
9.08289075e-01 1.25755863e-02 -9.07648146e-01 -4.64046210e-01
3.87853861e-01 8.98565769e-01 -2.62002219e-02 -2.41055027... | [11.432089805603027, 1.51131272315979] |
22b61cd0-734c-4362-a686-8b25b6d6ec81 | transdocanalyser-a-framework-for-offline-semi | 2306.02142 | null | https://arxiv.org/abs/2306.02142v1 | https://arxiv.org/pdf/2306.02142v1.pdf | TransDocAnalyser: A Framework for Offline Semi-structured Handwritten Document Analysis in the Legal Domain | State-of-the-art offline Optical Character Recognition (OCR) frameworks perform poorly on semi-structured handwritten domain-specific documents due to their inability to localize and label form fields with domain-specific semantics. Existing techniques for semi-structured document analysis have primarily used datasets ... | ['Saptarshi Ghosh', 'Gaurav Harit', 'Sagar Chakraborty'] | 2023-06-03 | null | null | null | null | ['optical-character-recognition'] | ['computer-vision'] | [ 5.26485562e-01 -5.55943668e-01 -4.51929774e-03 -6.42705023e-01
-9.45696414e-01 -1.14152002e+00 6.60296142e-01 -1.99883571e-03
-3.46125484e-01 4.95417207e-01 9.69759375e-02 -4.75465328e-01
-1.34823695e-01 -5.18312693e-01 -5.68454027e-01 -5.06719232e-01
3.88149142e-01 6.84953809e-01 2.46315837e-01 -6.44559786... | [11.846712112426758, 2.5539159774780273] |
6f341d8f-6c72-4310-8fbe-9672e42fa81e | lile-look-in-depth-before-looking-elsewhere-a | 2203.01445 | null | https://arxiv.org/abs/2203.01445v2 | https://arxiv.org/pdf/2203.01445v2.pdf | LILE: Look In-Depth before Looking Elsewhere -- A Dual Attention Network using Transformers for Cross-Modal Information Retrieval in Histopathology Archives | The volume of available data has grown dramatically in recent years in many applications. Furthermore, the age of networks that used multiple modalities separately has practically ended. Therefore, enabling bidirectional cross-modality data retrieval capable of processing has become a requirement for many domains and d... | ['H. R Tizhoosh', 'Danial Maleki'] | 2022-03-02 | null | null | null | null | ['cross-modal-information-retrieval'] | ['miscellaneous'] | [ 3.17443162e-01 -2.96024948e-01 -3.06868136e-01 -2.44014278e-01
-5.95595896e-01 -2.81215131e-01 8.10966432e-01 4.31318641e-01
-5.83906412e-01 6.85174704e-01 2.82085627e-01 1.24503009e-01
-2.79183179e-01 -5.13734460e-01 -4.06267196e-01 -9.09051597e-01
3.76132220e-01 8.07321817e-03 1.50896683e-01 -1.84247829... | [13.043000221252441, 4.878950595855713] |
2bb81444-d8c1-401a-8ce8-604d3301b85a | are-diffusion-models-vision-and-language | 2305.16397 | null | https://arxiv.org/abs/2305.16397v1 | https://arxiv.org/pdf/2305.16397v1.pdf | Are Diffusion Models Vision-And-Language Reasoners? | Text-conditioned image generation models have recently shown immense qualitative success using denoising diffusion processes. However, unlike discriminative vision-and-language models, it is a non-trivial task to subject these diffusion-based generative models to automatic fine-grained quantitative evaluation of high-l... | ['Siva Reddy', 'Christopher Pal', 'Vikram Voleti', 'Elinor Poole-Dayan', 'Benno Krojer'] | 2023-05-25 | null | null | null | null | ['text-matching'] | ['natural-language-processing'] | [ 3.12748104e-01 -1.90786645e-01 1.93764940e-01 -3.24426174e-01
-1.13860273e+00 -7.37637103e-01 1.54506969e+00 -3.23477030e-01
-4.58620667e-01 4.19096529e-01 5.85264921e-01 -2.06297338e-01
6.77116364e-02 -4.41004246e-01 -6.01954937e-01 -8.59505892e-01
2.19884053e-01 7.85522699e-01 3.01654756e-01 -2.40206018... | [11.276163101196289, -0.02103799767792225] |
4f5dde2d-799d-4224-b85d-0deada0b1879 | granngan-graph-annotation-generative | 2212.00449 | null | https://arxiv.org/abs/2212.00449v1 | https://arxiv.org/pdf/2212.00449v1.pdf | GrannGAN: Graph annotation generative adversarial networks | We consider the problem of modelling high-dimensional distributions and generating new examples of data with complex relational feature structure coherent with a graph skeleton. The model we propose tackles the problem of generating the data features constrained by the specific graph structure of each data point by spl... | ['Alexandros Kalousis', 'Magda Gregorova', 'Yoann Boget'] | 2022-12-01 | null | null | null | null | ['graph-matching'] | ['graphs'] | [ 3.16645652e-01 7.36829996e-01 2.23105133e-01 -3.45952481e-01
-5.12255549e-01 -6.98477805e-01 1.04871428e+00 2.70272136e-01
-1.33900762e-01 7.83889353e-01 -6.20744228e-02 -1.56362832e-01
-3.17235708e-01 -1.22677255e+00 -8.61604452e-01 -8.15728903e-01
-2.96620965e-01 1.16495812e+00 1.14672780e-01 -1.92994088... | [6.979259490966797, 6.010952472686768] |
ca46b1b9-b698-401f-9ade-aaf7ad65886d | optimal-prediction-using-expert-advice-and | 2302.13849 | null | https://arxiv.org/abs/2302.13849v2 | https://arxiv.org/pdf/2302.13849v2.pdf | Optimal Prediction Using Expert Advice and Randomized Littlestone Dimension | A classical result in online learning characterizes the optimal mistake bound achievable by deterministic learners using the Littlestone dimension (Littlestone '88). We prove an analogous result for randomized learners: we show that the optimal expected mistake bound in learning a class $\mathcal{H}$ equals its randomi... | ['Shay Moran', 'Idan Mehalel', 'Steve Hanneke', 'Yuval Filmus'] | 2023-02-27 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [-3.10732186e-01 4.44320917e-01 5.72389318e-03 -1.63793772e-01
-9.65128005e-01 -9.38413739e-01 -2.21521303e-01 1.40348703e-01
-7.44118154e-01 9.29915249e-01 -4.36802685e-01 -7.47920036e-01
-6.95615292e-01 -1.13053679e+00 -9.54218745e-01 -8.68717372e-01
-4.36494917e-01 3.62512887e-01 4.07745034e-01 -2.96656340... | [6.279873847961426, 4.475833892822266] |
98b8e1b8-968e-4af6-85f0-84bb617b3f21 | qumos-a-framework-for-preserving-security-of | 2304.11511 | null | https://arxiv.org/abs/2304.11511v1 | https://arxiv.org/pdf/2304.11511v1.pdf | QuMoS: A Framework for Preserving Security of Quantum Machine Learning Model | Security has always been a critical issue in machine learning (ML) applications. Due to the high cost of model training -- such as collecting relevant samples, labeling data, and consuming computing power -- model-stealing attack is one of the most fundamental but vitally important issues. When it comes to quantum comp... | ['Weiwen Jiang', 'Elizabeth Iwasawa', 'Blake Gage', 'Zhirui Hu', 'Jinyang Li', 'Zhepeng Wang'] | 2023-04-23 | null | null | null | null | ['architecture-search'] | ['methodology'] | [ 4.77932207e-02 2.95528490e-02 -1.32572949e-01 -2.20337540e-01
-9.98428106e-01 -8.45068991e-01 1.52974680e-01 -8.74357969e-02
-6.77664697e-01 6.89154506e-01 -7.80659199e-01 -7.22871482e-01
-1.38919488e-01 -1.21397603e+00 -8.92569244e-01 -1.20375025e+00
2.51769990e-01 7.42608905e-01 -3.62555729e-03 -2.27010995... | [5.79994010925293, 6.708191394805908] |
726a87e3-a271-4749-9b11-854eda37f227 | skill-based-few-shot-selection-for-in-context | 2305.14210 | null | https://arxiv.org/abs/2305.14210v1 | https://arxiv.org/pdf/2305.14210v1.pdf | Skill-Based Few-Shot Selection for In-Context Learning | In-Context learning is the paradigm that adapts large language models to downstream tasks by providing a few examples. Few-shot selection -- selecting appropriate examples for each test instance separately -- is important for in-context learning. In this paper, we propose Skill-KNN, a skill-based few-shot selection met... | ['Jian-Guang Lou', 'Weizhu Chen', 'Nanning Zheng', 'Bei Chen', 'Qiang Fu', 'Zeqi Lin', 'Bo Zhou', 'Shengnan An'] | 2023-05-23 | null | null | null | null | ['semantic-parsing'] | ['natural-language-processing'] | [ 3.35583270e-01 1.61887184e-01 -3.31491798e-01 -7.48532951e-01
-1.17643452e+00 -4.29438382e-01 3.97873461e-01 4.97947305e-01
-7.78330386e-01 6.68314278e-01 3.76635015e-01 -2.85348207e-01
-2.19136149e-01 -8.83089602e-01 -5.51701903e-01 -4.26871359e-01
-3.26347686e-02 6.16072595e-01 5.90406716e-01 -3.19456965... | [10.774259567260742, 8.153935432434082] |
7de7e6c6-1a33-4a71-b530-ed2c5fb83a96 | am-i-fit-for-this-physical-activity-neural | 2103.12095 | null | https://arxiv.org/abs/2103.12095v2 | https://arxiv.org/pdf/2103.12095v2.pdf | Am I fit for this physical activity? Neural embedding of physical conditioning from inertial sensors | Inertial Measurement Unit (IMU) sensors are present in everyday devices such as smartphones and fitness watches. As a result, the array of health-related research and applications that tap onto this data has been growing, but little attention has been devoted to the prediction of an individual's heart rate (HR) from IM... | ['Fabricio Murai', 'Davi Pedrosa de Aguiar'] | 2021-03-22 | null | null | null | null | ['photoplethysmography-ppg'] | ['medical'] | [ 6.32676601e-01 2.07999408e-01 -2.71523893e-01 -9.54628289e-02
-5.56950092e-01 2.34457869e-02 2.27826133e-01 -5.45860454e-03
-4.64168847e-01 8.46916795e-01 4.54545230e-01 -2.32034579e-01
4.63865064e-02 -7.50532269e-01 -7.20112383e-01 -6.13133848e-01
-1.97984442e-01 -1.17997512e-01 -2.63628036e-01 -2.23773569... | [13.783196449279785, 3.0322158336639404] |
bdbf0694-f772-4201-b80c-ac1050f43bf2 | learning-from-explicit-and-implicit | null | null | https://aclanthology.org/D16-1029 | https://aclanthology.org/D16-1029.pdf | Learning from Explicit and Implicit Supervision Jointly For Algebra Word Problems | null | ['Wen-tau Yih', 'Kai-Wei Chang', 'Ming-Wei Chang', 'Shyam Upadhyay'] | 2016-11-01 | null | null | null | emnlp-2016-11 | ['math-word-problem-solving', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'reasoning', 'time-series'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.405281066894531, 3.5897560119628906] |
b93fe4d3-a181-45a2-aed6-ed654abb9247 | conical-classification-for-efficient-one | null | null | https://aclanthology.org/2021.findings-emnlp.143 | https://aclanthology.org/2021.findings-emnlp.143.pdf | Conical Classification For Efficient One-Class Topic Determination | As the Internet grows in size, so does the amount of text based information that exists. For many application spaces it is paramount to isolate and identify texts that relate to a particular topic. While one-class classification would be ideal for such analysis, there is a relative lack of research regarding efficient ... | ['Sameer Khanna'] | null | null | null | null | findings-emnlp-2021-11 | ['one-class-classification'] | ['miscellaneous'] | [ 3.20975155e-01 -2.85080254e-01 -3.71154457e-01 -1.56644896e-01
-7.43795097e-01 -9.72519457e-01 8.54076743e-01 6.82830095e-01
-3.96462679e-01 4.81411487e-01 2.38197863e-01 -9.17668939e-01
-5.00481963e-01 -8.41960371e-01 -4.33857329e-02 -4.45099503e-01
-4.23801169e-02 7.63645887e-01 3.38195622e-01 -9.00344402... | [10.315600395202637, 7.414867401123047] |
82c00ca0-c08b-4a5e-8689-081d86ead713 | temporal-consistent-3d-lidar-representation | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Nunes_Temporal_Consistent_3D_LiDAR_Representation_Learning_for_Semantic_Perception_in_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Nunes_Temporal_Consistent_3D_LiDAR_Representation_Learning_for_Semantic_Perception_in_CVPR_2023_paper.pdf | Temporal Consistent 3D LiDAR Representation Learning for Semantic Perception in Autonomous Driving | Semantic perception is a core building block in autonomous driving, since it provides information about the drivable space and location of other traffic participants. For learning-based perception, often a large amount of diverse training data is necessary to achieve high performance. Data labeling is usually a bot... | ['Cyrill Stachniss', 'Jens Behley', 'Xieyuanli Chen', 'Rodrigo Marcuzzi', 'Louis Wiesmann', 'Lucas Nunes'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['panoptic-segmentation'] | ['computer-vision'] | [ 8.51318538e-02 -1.02331892e-01 -4.48659003e-01 -9.09930170e-01
-7.21745968e-01 -4.49923962e-01 4.03024584e-01 2.32640192e-01
-4.65878069e-01 3.04473698e-01 -4.88365382e-01 -3.28394502e-01
5.28527610e-02 -9.82030571e-01 -1.00030279e+00 -5.95365644e-01
7.70351887e-02 1.04642487e+00 8.34828854e-01 -1.92028001... | [8.163583755493164, -2.640207529067993] |
5184e874-868e-447a-8b2d-d7c0a7e5fcc7 | semi-supervised-2d-human-pose-estimation | 2303.04346 | null | https://arxiv.org/abs/2303.04346v1 | https://arxiv.org/pdf/2303.04346v1.pdf | Semi-Supervised 2D Human Pose Estimation Driven by Position Inconsistency Pseudo Label Correction Module | In this paper, we delve into semi-supervised 2D human pose estimation. The previous method ignored two problems: (i) When conducting interactive training between large model and lightweight model, the pseudo label of lightweight model will be used to guide large models. (ii) The negative impact of noise pseudo labels o... | ['Jieping Ye', 'Weihong Deng', 'Xiangang Li', 'Yue Yang', 'Hongbo Tian', 'Yulong Li', 'Linzhi Huang'] | 2023-03-08 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Huang_Semi-Supervised_2D_Human_Pose_Estimation_Driven_by_Position_Inconsistency_Pseudo_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Huang_Semi-Supervised_2D_Human_Pose_Estimation_Driven_by_Position_Inconsistency_Pseudo_CVPR_2023_paper.pdf | cvpr-2023-1 | ['2d-human-pose-estimation', 'pseudo-label'] | ['computer-vision', 'miscellaneous'] | [-2.36807689e-01 1.16627514e-01 -1.64206196e-02 -2.69888759e-01
-7.81636715e-01 -2.79133707e-01 9.11434814e-02 -4.22880910e-02
-5.53417325e-01 5.52066863e-01 -1.18828157e-03 2.51095772e-01
7.99742043e-02 -4.82351124e-01 -7.89694369e-01 -5.86932659e-01
8.31978545e-02 7.91543365e-01 7.36992121e-01 -2.91495204... | [7.032520294189453, -0.842252790927887] |
821be728-a65c-485b-a4ca-9043f36d9f7f | detecting-drug-drug-interactions-using | 1903.04571 | null | https://arxiv.org/abs/1903.04571v2 | https://arxiv.org/pdf/1903.04571v2.pdf | Detecting drug-drug interactions using artificial neural networks and classic graph similarity measures | Drug-drug interactions are preventable causes of medical injuries and often result in doctor and emergency room visits. Computational techniques can be used to predict potential drug-drug interactions. We approach the drug-drug interaction prediction problem as a link prediction problem and present two novel methods fo... | ['Guy Shtar', 'Lior Rokach', 'Bracha Shapira'] | 2019-03-11 | null | null | null | null | ['graph-similarity'] | ['graphs'] | [ 5.37343025e-02 1.93934008e-01 -6.91844523e-01 4.16438008e-04
-3.73648256e-01 -2.65620291e-01 3.64089251e-01 9.86746192e-01
-2.25170523e-01 1.01591861e+00 3.16795677e-01 -7.87041724e-01
-7.70825982e-01 -9.96104419e-01 -6.98272347e-01 -4.49583650e-01
-7.79860914e-01 5.81548631e-01 -1.27897635e-01 1.66705418... | [5.448053359985352, 5.894402503967285] |
1e45c9f7-d189-4281-8afc-09abc3f9a69e | dct-centered-temporal-relation-extraction | null | null | https://aclanthology.org/2022.coling-1.182 | https://aclanthology.org/2022.coling-1.182.pdf | DCT-Centered Temporal Relation Extraction | Most previous work on temporal relation extraction only focused on extracting the temporal relations among events or suffered from the issue of different expressions of events, timexes and Document Creation Time (DCT). Moreover, DCT can act as a hub to semantically connect the other events and timexes in a document. Un... | ['Sheng Xu', 'Peifeng Li', 'Liang Wang'] | null | null | null | null | coling-2022-10 | ['temporal-relation-extraction', 'temporal-relation-classification'] | ['natural-language-processing', 'natural-language-processing'] | [-1.21267036e-01 -3.17937136e-01 -5.29514074e-01 -5.36212862e-01
-6.27748609e-01 -6.33117199e-01 1.06830418e+00 3.40862453e-01
-1.15178786e-01 4.18878525e-01 7.32773721e-01 -2.02455387e-01
-2.86357671e-01 -7.23976195e-01 -3.44336689e-01 -5.29380143e-01
-3.32997620e-01 8.62401426e-02 5.18272102e-01 -1.28654033... | [9.07868480682373, 9.116676330566406] |
d610fa83-c4de-4e6b-a41e-8a9c03af793d | pre-trained-and-attention-based-neural | 2004.01940 | null | https://arxiv.org/abs/2004.01940v1 | https://arxiv.org/pdf/2004.01940v1.pdf | Pre-Trained and Attention-Based Neural Networks for Building Noetic Task-Oriented Dialogue Systems | The NOESIS II challenge, as the Track 2 of the 8th Dialogue System Technology Challenges (DSTC 8), is the extension of DSTC 7. This track incorporates new elements that are vital for the creation of a deployed task-oriented dialogue system. This paper describes our systems that are evaluated on all subtasks under this ... | ['Yu-Ping Ruan', 'Zhen-Hua Ling', 'Quan Liu', 'Jia-Chen Gu', 'Xiaodan Zhu', 'Tianda Li'] | 2020-04-04 | null | null | null | null | ['conversation-disentanglement'] | ['natural-language-processing'] | [-1.99104816e-01 7.36365139e-01 3.34494948e-01 -3.62823755e-01
-5.69783688e-01 -7.78042674e-01 1.05847073e+00 -1.48185223e-01
-4.28601533e-01 9.34450209e-01 5.78981638e-01 -5.55302143e-01
7.15257823e-02 -2.59814896e-02 -8.35934421e-04 -9.99214351e-02
1.12055406e-01 9.13492322e-01 3.76565784e-01 -1.17657804... | [12.875768661499023, 8.0330171585083] |
de8dddd5-b0a5-4d20-9435-d87766cf64b7 | lstm-based-system-call-language-modeling-and | 1611.01726 | null | http://arxiv.org/abs/1611.01726v1 | http://arxiv.org/pdf/1611.01726v1.pdf | LSTM-Based System-Call Language Modeling and Robust Ensemble Method for Designing Host-Based Intrusion Detection Systems | In computer security, designing a robust intrusion detection system is one of
the most fundamental and important problems. In this paper, we propose a
system-call language-modeling approach for designing anomaly-based host
intrusion detection systems. To remedy the issue of high false-alarm rates
commonly arising in co... | ['Jangho Lee', 'Yunheung Paek', 'Gyuwan Kim', 'Sungroh Yoon', 'Hayoon Yi'] | 2016-11-06 | null | null | null | null | ['computer-security'] | ['miscellaneous'] | [ 1.36246577e-01 -7.06544876e-01 -2.43886292e-01 -2.83774018e-01
-3.57208028e-02 -4.28355187e-01 6.84190273e-01 5.02520382e-01
-2.42119819e-01 3.64120424e-01 -3.82322609e-01 -7.44069338e-01
-2.91130930e-01 -7.64871597e-01 -1.13649994e-01 -5.28260887e-01
-2.63220429e-01 7.93575123e-02 3.43339205e-01 -3.95526111... | [5.278797626495361, 7.203211307525635] |
b7e1b707-e9bf-40e1-883d-96d792cfcf07 | local-connectivity-based-density-estimation | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Shin_Local_Connectivity-Based_Density_Estimation_for_Face_Clustering_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Shin_Local_Connectivity-Based_Density_Estimation_for_Face_Clustering_CVPR_2023_paper.pdf | Local Connectivity-Based Density Estimation for Face Clustering | Recent graph-based face clustering methods predict the connectivity of enormous edges, including false positive edges that link nodes with different classes. However, those false positive edges, which connect negative node pairs, have the risk of integration of different clusters when their connectivity is incorrec... | ['Yeong Jun Koh', 'Daehyun Kim', 'Jong-Hyeon Baek', 'Hyunseop Kim', 'Hyo-Jun Lee', 'Junho Shin'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['image-clustering', 'face-clustering', 'connectivity-estimation'] | ['computer-vision', 'computer-vision', 'graphs'] | [-4.63216066e-01 1.21935479e-01 -3.23170155e-01 -4.77550864e-01
-3.17534447e-01 -4.44664180e-01 1.67871773e-01 5.50062954e-02
1.18201897e-01 6.21760070e-01 -1.54232830e-01 5.34229912e-02
-4.44609940e-01 -1.11944950e+00 -4.19702828e-01 -7.14435101e-01
-3.64294678e-01 7.74417698e-01 3.02635372e-01 4.63889092... | [13.464239120483398, 1.0491267442703247] |
6aafbfb8-d929-4a9b-b1ae-02224c1682f2 | crowd-counting-by-adapting-convolutional | 1611.06748 | null | http://arxiv.org/abs/1611.06748v1 | http://arxiv.org/pdf/1611.06748v1.pdf | Crowd Counting by Adapting Convolutional Neural Networks with Side Information | Computer vision tasks often have side information available that is helpful
to solve the task. For example, for crowd counting, the camera perspective
(e.g., camera angle and height) gives a clue about the appearance and scale of
people in the scene. While side information has been shown to be useful for
counting syste... | ['Debarun Dhar', 'Antoni B. Chan', 'Di Kang'] | 2016-11-21 | null | null | null | null | ['image-deconvolution'] | ['computer-vision'] | [-2.84290314e-01 -5.47742248e-01 3.51939380e-01 -5.27495861e-01
1.64396062e-01 -4.95904684e-01 5.77768445e-01 8.89791325e-02
-1.03384829e+00 3.22812557e-01 3.33929986e-01 9.40659568e-02
4.52929169e-01 -9.29889321e-01 -5.40819943e-01 -7.12300837e-01
3.57267916e-01 2.23932713e-01 3.78705204e-01 -3.47614169... | [8.38121223449707, -0.24604520201683044] |
f262a60f-10c3-48ed-98ba-3b397a8feec2 | geometric-relational-embeddings-a-survey | 2304.11949 | null | https://arxiv.org/abs/2304.11949v1 | https://arxiv.org/pdf/2304.11949v1.pdf | Geometric Relational Embeddings: A Survey | Geometric relational embeddings map relational data as geometric objects that combine vector information suitable for machine learning and structured/relational information for structured/relational reasoning, typically in low dimensions. Their preservation of relational structures and their appealing properties and in... | ['Steffen Staab', 'Shirui Pan', 'Michael Cochez', 'Yunjie He', 'Ming Jin', 'Mojtaba Nayyeri', 'Bo Xiong'] | 2023-04-24 | null | null | null | null | ['knowledge-graph-completion', 'relational-reasoning'] | ['knowledge-base', 'natural-language-processing'] | [-9.86482948e-02 8.08132470e-01 -6.36946797e-01 -4.77385849e-01
-1.07896939e-01 -7.13734746e-01 8.21996868e-01 9.58483875e-01
-4.16463092e-02 1.79597452e-01 7.55042374e-01 -5.50302863e-01
-9.41790640e-01 -1.43893850e+00 -4.58130926e-01 -3.80647212e-01
-2.92948335e-01 8.87050033e-01 2.84801602e-01 -3.10224652... | [8.74554443359375, 7.7510833740234375] |
1aeaf599-6ce8-4893-8642-9a4fa000b98b | weak-multi-view-supervision-for-surface | 2105.01388 | null | https://arxiv.org/abs/2105.01388v1 | https://arxiv.org/pdf/2105.01388v1.pdf | Weak Multi-View Supervision for Surface Mapping Estimation | We propose a weakly-supervised multi-view learning approach to learn category-specific surface mapping without dense annotations. We learn the underlying surface geometry of common categories, such as human faces, cars, and airplanes, given instances from those categories. While traditional approaches solve this proble... | ['Stefan Holzer', 'Matteo Munaro', 'Rodrigo Ortiz Cayon', 'Srinivas Rao', 'Aidas Liaudanskas', 'Nishant Rai'] | 2021-05-04 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [ 2.36951664e-01 2.47494265e-01 -3.85731272e-02 -5.94674766e-01
-9.68362212e-01 -8.45344245e-01 8.04076314e-01 -5.12204617e-02
2.21598595e-01 2.64107108e-01 3.27928774e-02 3.88537258e-01
2.72780687e-01 -8.98413241e-01 -1.24755883e+00 -3.83841842e-01
3.67426097e-01 9.50131714e-01 4.71131444e-01 -2.00277403... | [8.490880966186523, -3.0559473037719727] |
ebf45e6d-92b9-46ee-9da4-b2fe07de7939 | a-sequential-quadratic-programming-approach | 2203.16478 | null | https://arxiv.org/abs/2203.16478v2 | https://arxiv.org/pdf/2203.16478v2.pdf | A Sequential Quadratic Programming Approach to the Solution of Open-Loop Generalized Nash Equilibria | Dynamic games can be an effective approach to modeling interactive behavior between multiple non-cooperative agents and they provide a theoretical framework for simultaneous prediction and control in such scenarios. In this work, we propose a numerical method for the solution of local generalized Nash equilibria (GNE) ... | ['Francesco Borrelli', 'Edward L. Zhu'] | 2022-03-30 | null | null | null | null | ['carracing-v0'] | ['playing-games'] | [-2.39909142e-01 5.09911180e-02 2.06399336e-02 2.42087275e-01
-7.42167294e-01 -6.74040794e-01 1.31933793e-01 6.26445264e-02
-5.58717012e-01 7.62503326e-01 -4.02240783e-01 -4.61320311e-01
-5.27793407e-01 -6.70367301e-01 -7.38409102e-01 -6.43047094e-01
-2.82099903e-01 4.97880191e-01 4.40180451e-01 -8.82212162... | [4.165401935577393, 2.5674657821655273] |
280f347c-a2ab-469e-9369-960a766b7269 | gorilla-large-language-model-connected-with | 2305.15334 | null | https://arxiv.org/abs/2305.15334v1 | https://arxiv.org/pdf/2305.15334v1.pdf | Gorilla: Large Language Model Connected with Massive APIs | Large Language Models (LLMs) have seen an impressive wave of advances recently, with models now excelling in a variety of tasks, such as mathematical reasoning and program synthesis. However, their potential to effectively use tools via API calls remains unfulfilled. This is a challenging task even for today's state-of... | ['Joseph E. Gonzalez', 'Xin Wang', 'Tianjun Zhang', 'Shishir G. Patil'] | 2023-05-24 | null | null | null | null | ['program-synthesis', 'mathematical-reasoning'] | ['computer-code', 'natural-language-processing'] | [-3.09094965e-01 -9.91586521e-02 -7.84533694e-02 -1.65783793e-01
-9.58312333e-01 -8.76040876e-01 8.65338206e-01 2.34729439e-01
6.89613521e-02 3.03119689e-01 2.46404380e-01 -8.03684056e-01
1.16546848e-03 -6.71612799e-01 -5.49308658e-01 5.34051061e-02
-5.95072694e-02 4.04994935e-01 4.64736931e-02 -3.96462113... | [8.204183578491211, 7.700591564178467] |
3f831938-42b7-4d6a-94fe-4527004e3009 | explainable-cardiac-pathology-classification | 1811.03433 | null | http://arxiv.org/abs/1811.03433v2 | http://arxiv.org/pdf/1811.03433v2.pdf | Explainable cardiac pathology classification on cine MRI with motion characterization by semi-supervised learning of apparent flow | We propose a method to classify cardiac pathology based on a novel approach
to extract image derived features to characterize the shape and motion of the
heart. An original semi-supervised learning procedure, which makes efficient
use of a large amount of non-segmented images and a small amount of images
segmented manu... | ['Hervé Delingette', 'Nicholas Ayache', 'Qiao Zheng'] | 2018-11-08 | null | null | null | null | ['cardiac-segmentation'] | ['medical'] | [ 2.74351388e-01 9.97366160e-02 -7.63599668e-03 -3.50125104e-01
-5.52245677e-01 -6.35859251e-01 2.46697381e-01 2.33550802e-01
-3.94401163e-01 6.16518199e-01 -1.96167052e-01 -2.05501258e-01
-1.90967828e-01 -5.05432069e-01 1.26326963e-01 -8.88191998e-01
-4.64291453e-01 6.53727055e-01 4.88966882e-01 3.94660354... | [14.247430801391602, -2.4324705600738525] |
7feae8fc-7bbc-4869-82db-86e1ef16e9ae | partial-advantage-estimator-for-proximal | 2301.10920 | null | https://arxiv.org/abs/2301.10920v1 | https://arxiv.org/pdf/2301.10920v1.pdf | Partial advantage estimator for proximal policy optimization | Estimation of value in policy gradient methods is a fundamental problem. Generalized Advantage Estimation (GAE) is an exponentially-weighted estimator of an advantage function similar to $\lambda$-return. It substantially reduces the variance of policy gradient estimates at the expense of bias. In practical application... | ['Simon Lucas', 'Greg Slabaugh', 'Yizhao Jin', 'Xiulei Song'] | 2023-01-26 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [-2.86417037e-01 -1.67750850e-01 -4.80274379e-01 -1.70842648e-01
-9.28350210e-01 -6.92318916e-01 3.79263252e-01 6.00460246e-02
-1.07689667e+00 1.20784521e+00 8.65795985e-02 -9.38185811e-01
-6.04622401e-02 -5.63386559e-01 -6.98650479e-01 -5.52049696e-01
-3.22660744e-01 3.34592611e-02 4.79571760e-01 -1.51670516... | [4.060157775878906, 2.457777261734009] |
f455902b-35db-4861-afd8-4fde9e70a6a7 | hierarchical-verbalizer-for-few-shot | 2305.16885 | null | https://arxiv.org/abs/2305.16885v1 | https://arxiv.org/pdf/2305.16885v1.pdf | Hierarchical Verbalizer for Few-Shot Hierarchical Text Classification | Due to the complex label hierarchy and intensive labeling cost in practice, the hierarchical text classification (HTC) suffers a poor performance especially when low-resource or few-shot settings are considered. Recently, there is a growing trend of applying prompts on pre-trained language models (PLMs), which has exhi... | ['Baoyuan Wang', 'Jingsheng Gao', 'Yixin Lian', 'Ke Ji'] | 2023-05-26 | null | null | null | null | ['few-shot-htc-1'] | ['natural-language-processing'] | [ 1.55721813e-01 2.34191790e-01 -6.93220317e-01 -3.35194886e-01
-6.55290067e-01 -1.86388254e-01 5.80406964e-01 3.55177701e-01
-5.70613325e-01 3.06218952e-01 5.69139719e-01 -3.12289864e-01
9.47950929e-02 -7.20896244e-01 -3.19309145e-01 -6.30660415e-01
3.57688576e-01 5.42511225e-01 2.56042421e-01 -2.71020055... | [10.724515914916992, 7.783438205718994] |
c35e6dc2-e2f2-4839-aa7e-30a7395cec61 | better-queries-for-aspect-category-sentiment | null | null | https://aclanthology.org/2020.ccl-1.100 | https://aclanthology.org/2020.ccl-1.100.pdf | Better Queries for Aspect-Category Sentiment Classification | Aspect-category sentiment classification (ACSC) aims to identify the sentiment polarities towards the aspect categories mentioned in a sentence. Because a sentence often mentions more than one aspect category and expresses different sentiment polarities to them, finding aspect category-related information from the sent... | ['Wu Xiaohui', 'Xu Siqi', 'Luo Jinchang', 'Zhong Huiqiang', 'Zhong Sheng-hua', 'Yin Cunxiang', 'Li Yuncong'] | null | null | null | null | ccl-2020-10 | ['aspect-category-detection'] | ['natural-language-processing'] | [ 1.24169432e-01 -1.83181971e-01 -5.26366949e-01 -6.10477984e-01
-8.23948324e-01 -7.42065668e-01 8.00387383e-01 6.67299509e-01
-2.38058478e-01 2.92137694e-02 7.46652305e-01 -4.20831770e-01
1.32390305e-01 -9.47771668e-01 -2.57685423e-01 -5.97656608e-01
5.15934110e-01 2.57772237e-01 5.35499044e-02 -6.67090714... | [11.430174827575684, 6.640318393707275] |
2cacea04-2f17-4091-90c9-9e3a897a1dff | sign-and-basis-invariant-networks-for | 2202.13013 | null | https://arxiv.org/abs/2202.13013v4 | https://arxiv.org/pdf/2202.13013v4.pdf | Sign and Basis Invariant Networks for Spectral Graph Representation Learning | We introduce SignNet and BasisNet -- new neural architectures that are invariant to two key symmetries displayed by eigenvectors: (i) sign flips, since if $v$ is an eigenvector then so is $-v$; and (ii) more general basis symmetries, which occur in higher dimensional eigenspaces with infinitely many choices of basis ei... | ['Stefanie Jegelka', 'Haggai Maron', 'Suvrit Sra', 'Tess Smidt', 'Lingxiao Zhao', 'Joshua Robinson', 'Derek Lim'] | 2022-02-25 | null | null | null | null | ['graph-regression'] | ['graphs'] | [ 2.67608017e-01 1.74318776e-01 -5.55163026e-01 7.28368247e-03
9.03117061e-02 -9.27718580e-01 4.76683259e-01 -5.29824905e-02
1.13318935e-01 6.87092125e-01 3.47802639e-01 -6.57790482e-01
-1.80567324e-01 -1.04429626e+00 -9.32044566e-01 -5.04347563e-01
-6.05390310e-01 3.84867102e-01 -1.46644175e-01 -5.48597336... | [6.87257719039917, 6.145992755889893] |
b59790a6-6118-455e-b0e3-60344316f5f5 | learning-accurate-performance-predictors-for | 2304.06393 | null | https://arxiv.org/abs/2304.06393v1 | https://arxiv.org/pdf/2304.06393v1.pdf | Learning Accurate Performance Predictors for Ultrafast Automated Model Compression | In this paper, we propose an ultrafast automated model compression framework called SeerNet for flexible network deployment. Conventional non-differen-tiable methods discretely search the desirable compression policy based on the accuracy from exhaustively trained lightweight models, and existing differentiable methods... | ['Jie zhou', 'Shengyu Liu', 'Han Xiao', 'Jiwen Lu', 'Ziwei Wang'] | 2023-04-13 | null | null | null | null | ['model-compression'] | ['methodology'] | [ 4.04816926e-01 2.55946219e-01 -7.06976831e-01 -4.17776972e-01
-6.26786947e-01 -1.17032580e-01 1.87126175e-01 6.22489303e-03
-5.67795038e-01 7.62682199e-01 -4.63456511e-01 -1.08687937e-01
-7.26402819e-01 -7.72449672e-01 -9.13375258e-01 -6.97153747e-01
1.23419881e-01 1.10263777e+00 6.25393018e-02 2.32116014... | [8.519527435302734, 3.184626817703247] |
6e820d0a-238f-4cf3-9193-318e4d57d8e6 | codex-a-comprehensive-knowledge-graph | 2009.07810 | null | https://arxiv.org/abs/2009.07810v2 | https://arxiv.org/pdf/2009.07810v2.pdf | CoDEx: A Comprehensive Knowledge Graph Completion Benchmark | We present CoDEx, a set of knowledge graph completion datasets extracted from Wikidata and Wikipedia that improve upon existing knowledge graph completion benchmarks in scope and level of difficulty. In terms of scope, CoDEx comprises three knowledge graphs varying in size and structure, multilingual descriptions of en... | ['Danai Koutra', 'Tara Safavi'] | 2020-09-16 | null | https://aclanthology.org/2020.emnlp-main.669 | https://aclanthology.org/2020.emnlp-main.669.pdf | emnlp-2020-11 | ['triple-classification'] | ['graphs'] | [-4.77153063e-01 5.60630620e-01 -7.29634583e-01 -1.56994984e-01
-5.13235033e-01 -8.80997121e-01 6.90212905e-01 5.33057570e-01
1.30362853e-01 1.01013756e+00 4.88427401e-01 -5.06986320e-01
-6.45506918e-01 -8.67351234e-01 -1.06112635e+00 2.84764677e-01
-5.13710022e-01 4.53530103e-01 1.48269951e-01 -2.09769368... | [8.881569862365723, 7.959242343902588] |
8df98b1a-a955-4206-af55-064e03dd5033 | managing-multi-facet-bias-in-collaborative | 2302.10575 | null | https://arxiv.org/abs/2302.10575v1 | https://arxiv.org/pdf/2302.10575v1.pdf | Managing multi-facet bias in collaborative filtering recommender systems | Due to the extensive growth of information available online, recommender systems play a more significant role in serving people's interests. Traditional recommender systems mostly use an accuracy-focused approach to produce recommendations. Today's research suggests that this single-dimension approach can lead the syst... | ['Saeed Farzi', 'Samira Vaez Barenji'] | 2023-02-21 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [-4.12332565e-01 -2.27871805e-01 -3.14542919e-01 -5.69595993e-01
-2.08416253e-01 -6.25388563e-01 5.57237983e-01 2.67288178e-01
-4.01289076e-01 5.37941933e-01 3.89643431e-01 -2.52482623e-01
-5.25455534e-01 -9.60046291e-01 -2.84221888e-01 -4.76838648e-01
1.25173971e-01 4.82072949e-01 3.38617980e-01 -6.35257840... | [9.92590618133545, 5.737848281860352] |
f5d771fe-7786-4df1-8297-078737ea3ab0 | on-the-use-of-deep-learning-for-blind-image | 1602.05531 | null | http://arxiv.org/abs/1602.05531v5 | http://arxiv.org/pdf/1602.05531v5.pdf | On the Use of Deep Learning for Blind Image Quality Assessment | In this work we investigate the use of deep learning for distortion-generic
blind image quality assessment. We report on different design choices, ranging
from the use of features extracted from pre-trained Convolutional Neural
Networks (CNNs) as a generic image description, to the use of features
extracted from a CNN ... | ['Raimondo Schettini', 'Paolo Napoletano', 'Simone Bianco', 'Luigi Celona'] | 2016-02-17 | null | null | null | null | ['blind-image-quality-assessment'] | ['computer-vision'] | [-2.02161282e-01 -2.63836890e-01 6.05333485e-02 -5.65973103e-01
-1.14010048e+00 -4.58617270e-01 3.83579671e-01 1.04551591e-01
-4.93179858e-01 4.68505919e-01 4.89185423e-01 5.15183620e-02
-2.70981312e-01 -5.69191575e-01 -4.38705266e-01 -6.07854605e-01
-1.63211420e-01 -1.88936424e-02 9.20699015e-02 -1.16511576... | [11.841264724731445, -1.8012531995773315] |
b954fea5-c286-4fbd-b323-ea9016f9c6b7 | class-agnostic-counting | 1811.00472 | null | http://arxiv.org/abs/1811.00472v1 | http://arxiv.org/pdf/1811.00472v1.pdf | Class-Agnostic Counting | Nearly all existing counting methods are designed for a specific object
class. Our work, however, aims to create a counting model able to count any
class of object. To achieve this goal, we formulate counting as a matching
problem, enabling us to exploit the image self-similarity property that
naturally exists in objec... | ['Weidi Xie', 'Erika Lu', 'Andrew Zisserman'] | 2018-11-01 | null | null | null | null | ['object-counting'] | ['computer-vision'] | [ 1.37898415e-01 -3.89450818e-01 -1.22617699e-01 -8.15828964e-02
-3.89408290e-01 -6.14508152e-01 7.16655254e-01 1.73771694e-01
-9.36405003e-01 5.22032559e-01 -2.11287469e-01 -1.46985635e-01
4.83397990e-01 -9.52971816e-01 -7.46393979e-01 -4.53228205e-01
1.62378669e-01 6.69751704e-01 8.26595306e-01 2.97053605... | [9.028463363647461, 0.40731653571128845] |
de625b38-f325-4849-b2e0-8505d565b645 | contextual-instance-decoupling-for-robust | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Wang_Contextual_Instance_Decoupling_for_Robust_Multi-Person_Pose_Estimation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Wang_Contextual_Instance_Decoupling_for_Robust_Multi-Person_Pose_Estimation_CVPR_2022_paper.pdf | Contextual Instance Decoupling for Robust Multi-Person Pose Estimation | Crowded scenes make it challenging to differentiate persons and locate their pose keypoints. This paper proposes the Contextual Instance Decoupling (CID), which presents a new pipeline for multi-person pose estimation. Instead of relying on person bounding boxes to spatially differentiate persons, CID decouples per... | ['Shiliang Zhang', 'Dongkai Wang'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['multi-person-pose-estimation'] | ['computer-vision'] | [-3.07837218e-01 -2.19879314e-01 4.25589293e-01 -1.55755118e-01
-8.00975621e-01 -7.32778788e-01 6.74421787e-01 1.98069826e-01
-8.39073956e-01 5.30578792e-01 4.18891191e-01 6.46604896e-01
6.95677102e-02 -4.17416930e-01 -4.77486044e-01 -4.58398044e-01
-6.61105663e-02 9.40362215e-01 5.38433015e-01 -1.08389609... | [7.208843231201172, -0.7794820070266724] |
a91368ef-f0bd-4160-a208-97401e43b362 | croc-cross-view-online-clustering-for-dense | 2303.13245 | null | https://arxiv.org/abs/2303.13245v1 | https://arxiv.org/pdf/2303.13245v1.pdf | CrOC: Cross-View Online Clustering for Dense Visual Representation Learning | Learning dense visual representations without labels is an arduous task and more so from scene-centric data. We propose to tackle this challenging problem by proposing a Cross-view consistency objective with an Online Clustering mechanism (CrOC) to discover and segment the semantics of the views. In the absence of hand... | ['Jean-Philippe Thiran', 'Tinne Tuytelaars', 'Behzad Bozorgtabar', 'Tim Lebailly', 'Thomas Stegmüller'] | 2023-03-23 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Stegmuller_CrOC_Cross-View_Online_Clustering_for_Dense_Visual_Representation_Learning_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Stegmuller_CrOC_Cross-View_Online_Clustering_for_Dense_Visual_Representation_Learning_CVPR_2023_paper.pdf | cvpr-2023-1 | ['online-clustering', 'unsupervised-semantic-segmentation', 'video-object-segmentation', 'video-semantic-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 2.10243016e-01 -1.05577216e-01 -3.00751012e-02 -4.15538996e-01
-8.13761115e-01 -1.01164770e+00 5.93180656e-01 3.91589314e-01
-2.05515593e-01 2.45414779e-01 -1.18316628e-01 -2.27288887e-01
-1.48005992e-01 -4.26619351e-01 -8.41773152e-01 -6.50220454e-01
2.12387815e-01 6.19751632e-01 2.51105845e-01 2.05744863... | [9.531218528747559, 0.7439717054367065] |
8532ad73-ea73-429f-b4bc-ca7793f488e3 | pesco-prompt-enhanced-self-contrastive | 2305.14963 | null | https://arxiv.org/abs/2305.14963v1 | https://arxiv.org/pdf/2305.14963v1.pdf | PESCO: Prompt-enhanced Self Contrastive Learning for Zero-shot Text Classification | We present PESCO, a novel contrastive learning framework that substantially improves the performance of zero-shot text classification. We formulate text classification as a neural text matching problem where each document is treated as a query, and the system learns the mapping from each query to the relevant class lab... | ['Yiming Yang', 'Ruohong Zhang', 'Ta-Chung Chi', 'Yau-Shian Wang'] | 2023-05-24 | null | null | null | null | ['text-matching'] | ['natural-language-processing'] | [ 2.72430152e-01 1.63868278e-01 -5.39336085e-01 -4.67030376e-01
-7.22845554e-01 -2.39413157e-01 8.71589541e-01 8.77958775e-01
-6.71896636e-01 4.68426168e-01 1.12642668e-01 -2.99984794e-02
1.61670130e-02 -1.16225576e+00 -4.77280736e-01 -4.18097973e-01
2.64111042e-01 9.07492161e-01 5.20527005e-01 -4.98671025... | [10.739142417907715, 7.456912994384766] |
cc13d834-5868-4e0d-9dbf-5ae09d842393 | few-shot-text-classification-with-edge | null | null | https://aclanthology.org/2020.coling-main.485 | https://aclanthology.org/2020.coling-main.485.pdf | Few-Shot Text Classification with Edge-Labeling Graph Neural Network-Based Prototypical Network | In this paper, we propose a new few-shot text classification method. Compared with supervised learning methods which require a large corpus of labeled documents, our method aims to make it possible to classify unlabeled text with few labeled data. To achieve this goal, we take advantage of advanced pre-trained language... | ['Ping Wang', 'Weijie Liu', 'Chen Lyu'] | 2020-12-01 | null | null | null | coling-2020-8 | ['few-shot-text-classification'] | ['natural-language-processing'] | [ 1.01363458e-01 1.16249710e-01 -4.22824711e-01 -7.33353257e-01
-2.64900148e-01 -3.94830972e-01 6.44557416e-01 5.97236633e-01
-4.46821243e-01 3.67737681e-01 -2.24579731e-03 -2.35087171e-01
1.05290338e-02 -9.56897855e-01 -1.49281397e-01 -4.60686892e-01
2.91325778e-01 4.38037634e-01 9.36733484e-02 -2.92502224... | [10.312446594238281, 6.855867385864258] |
21dc113c-f987-426f-817c-3e646bdcff1a | relpose-recovering-6d-poses-from-sparse-view | 2305.04926 | null | https://arxiv.org/abs/2305.04926v1 | https://arxiv.org/pdf/2305.04926v1.pdf | RelPose++: Recovering 6D Poses from Sparse-view Observations | We address the task of estimating 6D camera poses from sparse-view image sets (2-8 images). This task is a vital pre-processing stage for nearly all contemporary (neural) reconstruction algorithms but remains challenging given sparse views, especially for objects with visual symmetries and texture-less surfaces. We bui... | ['Shubham Tulsiani', 'Deva Ramanan', 'Jason Y. Zhang', 'Amy Lin'] | 2023-05-08 | null | null | null | null | ['pose-prediction', '3d-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 3.02893907e-01 2.25105301e-01 -2.36352626e-02 -4.94716287e-01
-7.17386425e-01 -8.64843845e-01 7.40426362e-01 -4.58753914e-01
-3.03296167e-02 5.51363006e-02 4.99269724e-01 -9.05845985e-02
8.53852630e-02 -4.13100749e-01 -1.14529479e+00 -3.59622329e-01
3.79672825e-01 9.36462104e-01 2.11698472e-01 -4.31647822... | [8.310474395751953, -2.7796108722686768] |
c4eb7dbb-ad20-407e-b345-ccc1a3157fef | does-the-geometry-of-word-embeddings-help-1 | 1705.10900 | null | http://arxiv.org/abs/1705.10900v1 | http://arxiv.org/pdf/1705.10900v1.pdf | Does the Geometry of Word Embeddings Help Document Classification? A Case Study on Persistent Homology Based Representations | We investigate the pertinence of methods from algebraic topology for text
data analysis. These methods enable the development of
mathematically-principled isometric-invariant mappings from a set of vectors to
a document embedding, which is stable with respect to the geometry of the
document in the selected metric space... | ['Paul Michel', 'Abhilasha Ravichander', 'Shruti Rijhwani'] | 2017-05-31 | null | null | null | null | ['document-embedding'] | ['methodology'] | [-2.78658479e-01 -8.50320980e-02 8.73555429e-03 -4.48256195e-01
-2.46366724e-01 -8.08208466e-01 1.00806606e+00 4.02692527e-01
-4.19626921e-01 3.68808061e-01 5.15751481e-01 -4.19173241e-01
-8.07430804e-01 -6.01747632e-01 2.02299319e-02 -8.04549634e-01
-1.21556856e-01 6.63779020e-01 -2.07098156e-01 -4.35518354... | [10.165270805358887, 7.573364734649658] |
b15447cb-86be-4486-886e-2ee5b1e75006 | 3d-semantic-scene-completion-a-survey | 2103.07466 | null | https://arxiv.org/abs/2103.07466v3 | https://arxiv.org/pdf/2103.07466v3.pdf | 3D Semantic Scene Completion: a Survey | Semantic Scene Completion (SSC) aims to jointly estimate the complete geometry and semantics of a scene, assuming partial sparse input. In the last years following the multiplication of large-scale 3D datasets, SSC has gained significant momentum in the research community because it holds unresolved challenges. Specifi... | ['Anne Verroust-Blondet', 'Raoul de Charette', 'Luis Roldao'] | 2021-03-12 | null | null | null | null | ['3d-semantic-scene-completion'] | ['computer-vision'] | [ 6.86318994e-01 5.27007103e-01 -1.72799807e-02 -3.61998558e-01
-6.30897820e-01 -6.25216961e-01 6.10729873e-01 -7.71662518e-02
-1.98404744e-01 4.78431851e-01 4.05941367e-01 9.81639549e-02
-2.73276716e-01 -3.77444506e-01 -4.39622670e-01 -4.66186196e-01
1.60219043e-03 4.84050602e-01 3.46494436e-01 -5.81327453... | [8.498708724975586, -2.695723056793213] |
c2dfca8c-b2b4-4821-afb7-6219e85a4ae6 | the-ties-that-matter-from-the-perspective-of | 2212.10960 | null | https://arxiv.org/abs/2212.10960v1 | https://arxiv.org/pdf/2212.10960v1.pdf | The Ties that matter: From the perspective of Similarity Measure in Online Social Networks | Online Social Networks have embarked on the importance of connection strength measures which has a broad array of applications such as, analyzing diffusion behaviors, community detection, link predictions, recommender systems. Though there are some existing connection strength measures, the density that a connection sh... | ['Anupam Biswas', 'Soumita Das'] | 2022-12-21 | null | null | null | null | ['community-detection'] | ['graphs'] | [-2.26318553e-01 -1.74542651e-01 -1.28011376e-01 3.38896154e-03
2.84356207e-01 -5.10939360e-01 4.98397738e-01 7.80909956e-01
-3.94814938e-01 6.98300898e-01 2.57098153e-02 -4.60697412e-01
-6.20085835e-01 -1.25581741e+00 1.00206554e-01 -4.51509982e-01
-7.21177697e-01 2.47268468e-01 6.94719136e-01 -4.23746705... | [6.985692024230957, 5.369307518005371] |
4b818f4a-b74d-405a-8430-accda697b363 | generative-prompt-tuning-for-relation | null | null | https://openreview.net/forum?id=lILSg7aInVH | https://openreview.net/pdf?id=lILSg7aInVH | Generative Prompt Tuning for Relation Classification | Prompt tuning is proposed to better tune pre-trained language models by filling the objective gap between the pre-training process and the downstream tasks. Current methods mainly convert the downstream tasks into masked language modeling (MLM) problems, which have proven effective for tasks with simple label sets. How... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['relation-classification', 'text-infilling'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.15768170e-01 5.69913745e-01 -5.42507529e-01 -6.90206528e-01
-8.88842702e-01 -6.13640904e-01 5.87848246e-01 2.10314453e-01
-3.34520996e-01 6.61023855e-01 3.55992675e-01 -6.28202856e-01
8.32076445e-02 -7.68074453e-01 -5.47491908e-01 -3.61416280e-01
1.77464947e-01 8.73938441e-01 -1.07803561e-01 -4.12868373... | [10.361760139465332, 8.552139282226562] |
1995f2a7-d30c-4ff7-bb8f-0372e5c8ab68 | dynamic-local-feature-aggregation-for | 2301.02836 | null | https://arxiv.org/abs/2301.02836v1 | https://arxiv.org/pdf/2301.02836v1.pdf | Dynamic Local Feature Aggregation for Learning on Point Clouds | Existing point cloud learning methods aggregate features from neighbouring points relying on constructing graph in the spatial domain, which results in feature update for each point based on spatially-fixed neighbours throughout layers. In this paper, we propose a dynamic feature aggregation (DFA) method that can trans... | ['Ran Wei', 'Hui Yuan', 'Pan Gao', 'Zihao Li'] | 2023-01-07 | null | null | null | null | ['point-cloud-classification'] | ['computer-vision'] | [-2.41566285e-01 -1.99925810e-01 -1.94124922e-01 -5.91400266e-01
-5.35282373e-01 -4.91801739e-01 2.92485088e-01 4.08469975e-01
-1.15184478e-01 3.92051935e-01 -7.48414919e-02 1.86420947e-01
-6.06814563e-01 -1.32761192e+00 -7.38219142e-01 -7.27841079e-01
-2.99098879e-01 2.41001248e-01 3.74643952e-01 2.05793962... | [7.940910339355469, -3.5425429344177246] |
b939a05f-1081-4bb0-8d47-019ea34f05d0 | scatter-selective-context-attentional-scene | 2003.11288 | null | https://arxiv.org/abs/2003.11288v1 | https://arxiv.org/pdf/2003.11288v1.pdf | SCATTER: Selective Context Attentional Scene Text Recognizer | Scene Text Recognition (STR), the task of recognizing text against complex image backgrounds, is an active area of research. Current state-of-the-art (SOTA) methods still struggle to recognize text written in arbitrary shapes. In this paper, we introduce a novel architecture for STR, named Selective Context ATtentional... | ['Roee Litman', 'Ron Litman', 'Shahar Tsiper', 'Oron Anschel', 'Shai Mazor', 'R. Manmatha'] | 2020-03-25 | scatter-selective-context-attentional-scene-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Litman_SCATTER_Selective_Context_Attentional_Scene_Text_Recognizer_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Litman_SCATTER_Selective_Context_Attentional_Scene_Text_Recognizer_CVPR_2020_paper.pdf | cvpr-2020-6 | ['irregular-text-recognition'] | ['computer-vision'] | [ 1.0412002 -0.45914298 0.13979013 -0.4031875 -0.41113845 -0.16094016
0.84871393 0.1652448 -0.5535889 0.29811034 0.21481119 -0.4593637
0.5291666 -0.5804057 -0.9029188 -0.91534823 0.55817634 0.23074536
0.6044696 0.02616489 0.7483121 0.41179934 -1.4487469 0.963551
0.64054114 1.0672098 0.383... | [11.888192176818848, 2.207007884979248] |
e191a96b-0a40-485f-8bf7-9fe453baed9b | confidence-aware-personalized-federated-1 | 2305.12557 | null | https://arxiv.org/abs/2305.12557v1 | https://arxiv.org/pdf/2305.12557v1.pdf | Confidence-aware Personalized Federated Learning via Variational Expectation Maximization | Federated Learning (FL) is a distributed learning scheme to train a shared model across clients. One common and fundamental challenge in FL is that the sets of data across clients could be non-identically distributed and have different sizes. Personalized Federated Learning (PFL) attempts to solve this challenge via lo... | ['Matthew B. Blaschko', 'Xingchen Ma', 'Junyi Zhu'] | 2023-05-21 | confidence-aware-personalized-federated | http://openaccess.thecvf.com//content/CVPR2023/html/Zhu_Confidence-Aware_Personalized_Federated_Learning_via_Variational_Expectation_Maximization_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhu_Confidence-Aware_Personalized_Federated_Learning_via_Variational_Expectation_Maximization_CVPR_2023_paper.pdf | cvpr-2023-1 | ['personalized-federated-learning'] | ['methodology'] | [-5.35735607e-01 -2.97152460e-01 -2.99211204e-01 -6.08301282e-01
-1.22200990e+00 -2.77928501e-01 4.31665152e-01 -3.30115825e-01
-1.73021257e-01 7.56177366e-01 2.99620092e-01 -1.32059619e-01
-3.83371919e-01 -5.61243713e-01 -7.32978165e-01 -1.04284167e+00
-3.62881012e-02 8.12276542e-01 1.56900033e-01 4.07527149... | [5.8201775550842285, 6.30317497253418] |
a8fd5b5b-fca6-4e99-a6a1-43a87d413b7b | a-scope-sensitive-and-result-attentive-model | 2211.12220 | null | https://arxiv.org/abs/2211.12220v1 | https://arxiv.org/pdf/2211.12220v1.pdf | A Scope Sensitive and Result Attentive Model for Multi-Intent Spoken Language Understanding | Multi-Intent Spoken Language Understanding (SLU), a novel and more complex scenario of SLU, is attracting increasing attention. Unlike traditional SLU, each intent in this scenario has its specific scope. Semantic information outside the scope even hinders the prediction, which tremendously increases the difficulty of ... | ['Weijia Jia', 'Wenmian Yang', 'Lizhi Cheng'] | 2022-11-22 | null | null | null | null | ['spoken-language-understanding', 'intent-detection', 'slot-filling', 'spoken-language-understanding'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'speech'] | [ 5.91924667e-01 1.71390459e-01 -2.30061516e-01 -3.82618755e-01
-9.88042057e-01 -3.82227689e-01 6.88356310e-02 3.70825566e-02
-4.19617862e-01 6.01174295e-01 6.09889507e-01 -2.77435929e-01
3.90897453e-01 -8.36727202e-01 -5.75902343e-01 -2.00777769e-01
4.71246213e-01 2.39274994e-01 4.30828154e-01 -3.68041426... | [12.566384315490723, 7.3690056800842285] |
8bd21ee7-e7f2-474e-88cb-45752b734727 | transfer-learning-for-personality-perception | 2305.16076 | null | https://arxiv.org/abs/2305.16076v2 | https://arxiv.org/pdf/2305.16076v2.pdf | Transfer Learning for Personality Perception via Speech Emotion Recognition | Holistic perception of affective attributes is an important human perceptual ability. However, this ability is far from being realized in current affective computing, as not all of the attributes are well studied and their interrelationships are poorly understood. In this work, we investigate the relationship between t... | ['Catherine Lai', 'Peter Bell', 'Yuanchao Li'] | 2023-05-25 | null | null | null | null | ['speech-emotion-recognition'] | ['speech'] | [-1.75536070e-02 -6.27331734e-02 5.75659983e-02 -7.69957066e-01
-1.66758686e-01 -4.26847637e-01 3.05620342e-01 2.16992557e-01
-2.02127337e-01 4.85210508e-01 5.49475551e-01 3.31645876e-01
-1.93717331e-02 -6.54361963e-01 1.34890243e-01 -5.14951229e-01
7.69516155e-02 1.36862844e-01 -4.41550761e-01 -4.10499483... | [13.060680389404297, 5.7190375328063965] |
7e8fa824-1ed5-482a-a3cc-554170c792a5 | 190910393 | 1909.10393 | null | https://arxiv.org/abs/1909.10393v1 | https://arxiv.org/pdf/1909.10393v1.pdf | Specificity-Based Sentence Ordering for Multi-Document Extractive Risk Summarization | Risk mining technologies seek to find relevant textual extractions that capture entity-risk relationships. However, when high volume data sets are processed, a multitude of relevant extractions can be returned, shifting the focus to how best to present the results. We provide the details of a risk mining multi-document... | ['Eleanor Hagerman', 'Berk Ekmekci', 'Blake Howald'] | 2019-09-23 | null | null | null | null | ['sentence-ordering'] | ['natural-language-processing'] | [ 5.69726288e-01 7.44462013e-01 -4.58281457e-01 -1.38036907e-01
-1.23076153e+00 -8.77934873e-01 8.58707249e-01 8.28178644e-01
-3.62624168e-01 8.49511981e-01 1.19821966e+00 -3.14279824e-01
-6.22745097e-01 -5.89489698e-01 -1.50097147e-01 -2.24510670e-01
-1.15100339e-01 5.58479011e-01 8.99751186e-02 -3.11737150... | [12.390007019042969, 9.58570384979248] |
fc7ff9d5-7eb3-4f9d-9e9c-b508bc56a51e | improving-user-controlled-table-to-text | 2302.09820 | null | https://arxiv.org/abs/2302.09820v1 | https://arxiv.org/pdf/2302.09820v1.pdf | Improving User Controlled Table-To-Text Generation Robustness | In this work we study user controlled table-to-text generation where users explore the content in a table by selecting cells and reading a natural language description thereof automatically produce by a natural language generator. Such generation models usually learn from carefully selected cell combinations (clean cel... | ['Laura Perez-Beltrachini', 'Zhongyi Yu', 'Yunqing Liu', 'Hanxu Hu'] | 2023-02-20 | null | null | null | null | ['table-to-text-generation'] | ['natural-language-processing'] | [ 3.54670435e-01 2.52906352e-01 -3.64845902e-01 -5.73635567e-03
-1.54552078e+00 -9.05579507e-01 7.84852505e-01 4.59673136e-01
-3.01150650e-01 1.60096622e+00 4.42217112e-01 -1.18663043e-01
4.02800255e-02 -9.52752888e-01 -6.32696569e-01 -5.19955754e-01
2.53292657e-02 1.33060443e+00 1.05488002e-01 -5.10272205... | [11.655220985412598, 8.94571590423584] |
8c753ca2-1382-4efb-af51-19cca961bfcc | combining-task-and-dialogue-streams-in | null | null | https://aclanthology.org/W14-4316 | https://aclanthology.org/W14-4316.pdf | Combining Task and Dialogue Streams in Unsupervised Dialogue Act Models | null | ['Aysu Ezen-Can', 'Kristy Boyer'] | 2014-06-01 | null | null | null | ws-2014-6 | ['dialogue-act-classification'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.266854763031006, 3.6302547454833984] |
cdd6edf4-5fef-44d8-8dfc-9067a754e47a | decomposition-compression-and-synthesis-dcs | 2012.00650 | null | https://arxiv.org/abs/2012.00650v4 | https://arxiv.org/pdf/2012.00650v4.pdf | Decoder-side Cross Resolution Synthesis for Video Compression Enhancement | This paper proposes a decoder-side Cross Resolution Synthesis (CRS) module to pursue better compression efficiency beyond the latest Versatile Video Coding (VVC), where we encode intra frames at original high resolution (HR), compress inter frames at a lower resolution (LR), and then super-resolve decoded LR inter fram... | ['zhenyu Dai', 'Dandan Ding', 'Dong Wang', 'Zhan Ma', 'Tong Chen', 'Ming Lu'] | 2020-12-01 | null | null | null | null | ['video-reconstruction'] | ['computer-vision'] | [ 6.32027805e-01 -1.81603074e-01 -3.51871789e-01 -8.29556286e-02
-8.62143576e-01 -2.96122760e-01 2.09020749e-01 -4.00579393e-01
-7.89712891e-02 8.44579101e-01 5.43526590e-01 -1.58837840e-01
-5.52428141e-02 -7.17930675e-01 -4.30728912e-01 -7.48896956e-01
-3.82896900e-01 -6.86810911e-01 6.52575433e-01 -2.95854867... | [11.059357643127441, -1.9572899341583252] |
45984123-c886-48a8-9c38-1faa33dcf569 | option-tracing-beyond-correctness-analysis-in | 2104.09043 | null | https://arxiv.org/abs/2104.09043v1 | https://arxiv.org/pdf/2104.09043v1.pdf | Option Tracing: Beyond Correctness Analysis in Knowledge Tracing | Knowledge tracing refers to a family of methods that estimate each student's knowledge component/skill mastery level from their past responses to questions. One key limitation of most existing knowledge tracing methods is that they can only estimate an \emph{overall} knowledge level of a student per knowledge component... | ['Andrew Lan', 'Jay Raspat', 'Aritra Ghosh'] | 2021-04-19 | null | null | null | null | ['skill-mastery'] | ['robots'] | [-1.35528475e-01 -1.21207252e-01 -2.62048095e-01 -5.63508272e-01
-7.37425864e-01 -1.06043589e+00 -7.73702860e-02 7.20104575e-01
-2.05005854e-01 7.25946307e-01 -5.31626586e-03 -9.80208516e-01
-5.98539412e-01 -1.06902957e+00 -6.11614645e-01 1.59025356e-01
9.25635040e-01 3.61538678e-01 4.18308914e-01 -7.59511739... | [10.130011558532715, 7.300505638122559] |
6b584eb9-9f16-4569-8200-e88868203a30 | traj-mae-masked-autoencoders-for-trajectory | 2303.06697 | null | https://arxiv.org/abs/2303.06697v1 | https://arxiv.org/pdf/2303.06697v1.pdf | Traj-MAE: Masked Autoencoders for Trajectory Prediction | Trajectory prediction has been a crucial task in building a reliable autonomous driving system by anticipating possible dangers. One key issue is to generate consistent trajectory predictions without colliding. To overcome the challenge, we propose an efficient masked autoencoder for trajectory prediction (Traj-MAE) th... | ['Pheng-Ann Heng', 'Guangyong Chen', 'Chenyong Guan', 'Jianye Hao', 'Furui Liu', 'Kun Shao', 'Jiaze Wang', 'Hao Chen'] | 2023-03-12 | null | null | null | null | ['trajectory-prediction'] | ['computer-vision'] | [-3.02722812e-01 -1.82578430e-01 -6.93493560e-02 -2.83370316e-01
-4.74194497e-01 -1.75644591e-01 7.96102107e-01 -1.39061734e-01
-4.36690986e-01 8.00905824e-01 4.51559305e-01 -1.99190676e-01
2.82853050e-03 -9.15919483e-01 -9.96616721e-01 -6.90076590e-01
-4.72777694e-01 5.52226901e-01 5.62982798e-01 -6.48567319... | [5.882587432861328, 0.7912087440490723] |
86042f48-9761-4a45-8d4a-54276a6fe53a | noisy-deductive-reasoning-how-humans | 2012.08298 | null | https://arxiv.org/abs/2012.08298v1 | https://arxiv.org/pdf/2012.08298v1.pdf | Noisy Deductive Reasoning: How Humans Construct Math, and How Math Constructs Universes | We present a computational model of mathematical reasoning according to which mathematics is a fundamentally stochastic process. That is, on our model, whether or not a given formula is deemed a theorem in some axiomatic system is not a matter of certainty, but is instead governed by a probability distribution. We then... | ['David Kinney', 'David H. Wolpert'] | 2020-10-28 | null | null | null | null | ['mathematical-reasoning'] | ['natural-language-processing'] | [ 1.06204741e-01 4.98784184e-01 8.93203169e-02 -7.28736743e-02
1.13834376e-02 -6.83321238e-01 1.15921342e+00 2.24590793e-01
-2.27850765e-01 5.06248832e-01 2.25621670e-01 -1.15333903e+00
-7.53902018e-01 -1.33264005e+00 -8.58736753e-01 -3.71415824e-01
4.02825594e-01 5.67596257e-01 2.83079445e-01 -1.61727503... | [8.783854484558105, 6.602255821228027] |
967bc7bf-a0d6-4a01-9c80-98d1563ad7e3 | see-and-think-disentangling-semantic-scene | null | null | http://papers.nips.cc/paper/7310-see-and-think-disentangling-semantic-scene-completion | http://papers.nips.cc/paper/7310-see-and-think-disentangling-semantic-scene-completion.pdf | See and Think: Disentangling Semantic Scene Completion | Semantic scene completion predicts volumetric occupancy and object category of a 3D scene, which helps intelligent agents to understand and interact with the surroundings. In this work, we propose a disentangled framework, sequentially carrying out 2D semantic segmentation, 2D-3D reprojection and 3D semantic scene comp... | ['Xiaowei Li', 'Yinhe Han', 'Shice Liu', 'Yu Hu', 'Yiming Zeng', 'Qiankun Tang', 'Beibei Jin'] | 2018-12-01 | null | null | null | neurips-2018-12 | ['3d-semantic-scene-completion', '2d-semantic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 4.01982635e-01 1.94936320e-01 1.35374372e-03 -4.50295478e-01
-4.68760341e-01 -4.16387945e-01 6.47749186e-01 1.77156441e-02
-1.98372349e-01 4.32609618e-01 1.53388813e-01 -8.58149901e-02
-7.60061666e-02 -9.76166189e-01 -5.48709512e-01 -5.64140081e-01
-1.75094102e-02 7.19839454e-01 5.65850556e-01 -1.90596014... | [8.519922256469727, -2.8322134017944336] |
7b7804cc-52fc-4157-a2e7-121d6811eba3 | icnet-for-real-time-semantic-segmentation-on | 1704.08545 | null | http://arxiv.org/abs/1704.08545v2 | http://arxiv.org/pdf/1704.08545v2.pdf | ICNet for Real-Time Semantic Segmentation on High-Resolution Images | We focus on the challenging task of real-time semantic segmentation in this
paper. It finds many practical applications and yet is with fundamental
difficulty of reducing a large portion of computation for pixel-wise label
inference. We propose an image cascade network (ICNet) that incorporates
multi-resolution branche... | ['Xiaoyong Shen', 'Xiaojuan Qi', 'Hengshuang Zhao', 'Jiaya Jia', 'Jianping Shi'] | 2017-04-27 | icnet-for-real-time-semantic-segmentation-on-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Hengshuang_Zhao_ICNet_for_Real-Time_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Hengshuang_Zhao_ICNet_for_Real-Time_ECCV_2018_paper.pdf | eccv-2018-9 | ['thermal-image-segmentation', 'dichotomous-image-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.98948133e-01 -1.43125623e-01 -7.25409836e-02 -4.59798217e-01
-8.93865347e-01 -7.46586084e-01 2.61685640e-01 -1.22907571e-01
-6.97154880e-01 5.31326175e-01 -5.70195794e-01 -5.16161263e-01
3.61045480e-01 -9.49736238e-01 -5.97244024e-01 -4.75530416e-01
3.10216397e-01 5.18982053e-01 5.89731216e-01 -2.35872623... | [9.495227813720703, 0.1337227076292038] |
fe83f326-1f2f-4e99-bac4-dca245cbcf46 | bulbar-als-detection-based-on-analysis-of | 2003.10806 | null | https://arxiv.org/abs/2003.10806v1 | https://arxiv.org/pdf/2003.10806v1.pdf | Bulbar ALS Detection Based on Analysis of Voice Perturbation and Vibrato | On average the lack of biological markers causes a one year diagnostic delay to detect amyotrophic lateral sclerosis (ALS). To improve the diagnostic process an automatic voice assessment based on acoustic analysis can be used. The purpose of this work was to verify the sutability of the sustain vowel phonation test fo... | ['Alexander Petrovsky', 'Maxim Vashkevich', 'Yuliya Rushkevich'] | 2020-03-24 | null | null | null | null | ['als-detection'] | ['medical'] | [ 2.17964262e-01 -2.93164521e-01 2.33529657e-01 -1.14588171e-01
-8.39291930e-01 -3.47406954e-01 1.47417516e-01 -4.69391532e-02
-5.47122836e-01 1.00224817e+00 1.19038500e-01 -1.84741199e-01
-3.01980913e-01 -3.04726392e-01 2.53237605e-01 -7.20802784e-01
-4.36885990e-02 7.28250325e-01 3.43963534e-01 4.58846502... | [14.184368133544922, 5.358275890350342] |
f55f66e7-bee9-42ad-ae08-f42de568f5f0 | edface-celeb-1m-benchmarking-face | 2110.05031 | null | https://arxiv.org/abs/2110.05031v2 | https://arxiv.org/pdf/2110.05031v2.pdf | EDFace-Celeb-1M: Benchmarking Face Hallucination with a Million-scale Dataset | Recent deep face hallucination methods show stunning performance in super-resolving severely degraded facial images, even surpassing human ability. However, these algorithms are mainly evaluated on non-public synthetic datasets. It is thus unclear how these algorithms perform on public face hallucination datasets. Mean... | ['Stefanos Zafeiriou', 'Wei Liu', 'Jiankang Deng', 'Jingyu Liu', 'Wenhan Luo', 'Dongxu Li', 'Kaihao Zhang'] | 2021-10-11 | null | null | null | null | ['face-hallucination'] | ['computer-vision'] | [-9.09238905e-02 7.01808482e-02 -1.09724514e-01 -3.41348767e-01
-3.93930137e-01 -1.30765140e-02 5.73976994e-01 -8.56931746e-01
7.39834905e-02 9.91848648e-01 5.06440043e-01 7.12909251e-02
2.67046213e-01 -5.84486425e-01 -6.11649454e-01 -4.33641165e-01
2.27498785e-01 5.11871457e-01 -5.37683964e-01 -3.49093378... | [12.8594970703125, 0.18617454171180725] |
022030cb-0401-474c-9c70-f75f1ad0f835 | what-makes-for-good-views-for-contrastive | 2005.10243 | null | https://arxiv.org/abs/2005.10243v3 | https://arxiv.org/pdf/2005.10243v3.pdf | What Makes for Good Views for Contrastive Learning? | Contrastive learning between multiple views of the data has recently achieved state of the art performance in the field of self-supervised representation learning. Despite its success, the influence of different view choices has been less studied. In this paper, we use theoretical and empirical analysis to better under... | ['Cordelia Schmid', 'Chen Sun', 'Ben Poole', 'Yonglong Tian', 'Phillip Isola', 'Dilip Krishnan'] | 2020-05-20 | what-makes-for-good-views-for-contrastive-1 | http://proceedings.neurips.cc/paper/2020/hash/4c2e5eaae9152079b9e95845750bb9ab-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/4c2e5eaae9152079b9e95845750bb9ab-Paper.pdf | neurips-2020-12 | ['self-supervised-image-classification'] | ['computer-vision'] | [ 3.12384725e-01 1.85416773e-01 -3.56754512e-01 -5.31649828e-01
-7.21428573e-01 -6.44513726e-01 7.06755698e-01 2.59131081e-02
-4.99660999e-01 4.24626201e-01 2.35517263e-01 -1.57081872e-01
-6.33228570e-02 -6.45231247e-01 -8.23584855e-01 -6.14794731e-01
2.92357951e-01 4.65445697e-01 2.42112890e-01 -1.25477791... | [9.424147605895996, 2.351181983947754] |
1900a4e9-7743-446b-9fd4-22ca0621c76b | bi-level-dynamic-learning-for-jointly-multi | 2305.06720 | null | https://arxiv.org/abs/2305.06720v1 | https://arxiv.org/pdf/2305.06720v1.pdf | Bi-level Dynamic Learning for Jointly Multi-modality Image Fusion and Beyond | Recently, multi-modality scene perception tasks, e.g., image fusion and scene understanding, have attracted widespread attention for intelligent vision systems. However, early efforts always consider boosting a single task unilaterally and neglecting others, seldom investigating their underlying connections for joint p... | ['Risheng Liu', 'Xin Fan', 'Long Ma', 'Guanyao Wu', 'JinYuan Liu', 'Zhu Liu'] | 2023-05-11 | null | null | null | null | ['scene-understanding'] | ['computer-vision'] | [ 4.82877195e-01 -2.53130376e-01 1.07790582e-01 -5.29586911e-01
-8.89423668e-01 -3.53406549e-01 6.47160649e-01 -3.67890522e-02
-4.91315812e-01 5.01531720e-01 1.36528954e-01 -1.56575635e-01
-1.47847638e-01 -5.12016237e-01 -7.13046849e-01 -8.82297575e-01
5.24994195e-01 -2.45023414e-01 1.94977254e-01 -2.36251995... | [10.101576805114746, -1.156776785850525] |
8bccb532-155a-44fd-ad6d-69b7f2fbf565 | bias-mitigation-techniques-in-image | 2303.11449 | null | https://arxiv.org/abs/2303.11449v1 | https://arxiv.org/pdf/2303.11449v1.pdf | Bias mitigation techniques in image classification: fair machine learning in human heritage collections | A major problem with using automated classification systems is that if they are not engineered correctly and with fairness considerations, they could be detrimental to certain populations. Furthermore, while engineers have developed cutting-edge technologies for image classification, there is still a gap in the applica... | ['Christoph Nötzli', 'Erik Norén', 'Sushruth Badri', 'Dalia Ortiz Pablo'] | 2023-03-20 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 9.53092277e-02 1.76709667e-01 -1.92914065e-02 -6.45370841e-01
-2.90059656e-01 -2.68738985e-01 7.14350760e-01 1.58803180e-01
-9.53049779e-01 8.90664160e-01 2.82009900e-01 -2.36292891e-02
4.97182235e-02 -1.03812802e+00 -5.38112581e-01 -3.39009583e-01
-4.34905738e-02 4.95500028e-01 5.07947840e-02 -5.23795724... | [13.107145309448242, 1.2639724016189575] |
0740a040-4238-44ae-8043-1dcfd10b07ea | adaptive-course-recommendation-system | null | null | https://doi.org/10.1016/j.knosys.2021.107085 | https://drive.google.com/file/d/14hHF9YQ_PlMGtzE6jk4qOryLMmO_tLeQ/view?usp=sharing | Adaptive Course Recommendation System | In the process of course learning, users incline to change their interests with the improvements of their cognition. Existing course recommendation methods usually assume that users’ preferences are static. They fail to capture the user’s dynamic interests in sequential learning behaviours. In this respect, the recomme... | ['Pengcheng Wu', 'Yong liu', 'Wenhua Zeng', 'Fan Lin', 'Shibo Feng', 'Yuanguo Lin'] | 2021-07-19 | null | null | null | journal-2021-7 | ['hierarchical-reinforcement-learning'] | ['methodology'] | [-1.16885379e-01 -3.79879355e-01 -4.65740055e-01 -3.67818713e-01
-1.29799828e-01 -6.72554135e-01 3.33252341e-01 4.45559323e-01
-3.08546931e-01 4.05485421e-01 3.43419433e-01 -2.36534819e-01
-6.08438551e-01 -1.07663190e+00 -4.81714636e-01 -5.44109225e-01
1.35138407e-01 3.45741898e-01 6.09303117e-01 -5.69163918... | [10.15364933013916, 5.749889373779297] |
f82ab540-8076-4192-adc4-0e7d0399fa2a | alleviation-of-temperature-variation-induced | 2103.03111 | null | https://arxiv.org/abs/2103.03111v5 | https://arxiv.org/pdf/2103.03111v5.pdf | Alleviation of Temperature Variation Induced Accuracy Degradation in Ferroelectric FinFET Based Neural Network | This paper reports the impacts of temperature variation on the inference accuracy of pre-trained all-ferroelectric FinFET deep neural networks, along with plausible design techniques to abate these impacts. We adopted a pre-trained artificial neural network (N.N.) with 96.4% inference accuracy on the MNIST dataset as t... | ['Thomas Kämpfe', 'Darsen D. Lu', 'Md. Aftab Baig', 'Hoang-Hiep Le', 'Yao-Jen Lee', 'Sourav De'] | 2021-03-03 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [ 4.66637999e-01 -1.04049683e-01 4.60682921e-02 -5.97620308e-01
-3.26832891e-01 -5.66147745e-01 3.08891654e-01 1.91687960e-02
-7.40287483e-01 9.84249413e-01 -5.29420972e-01 -6.85005367e-01
-4.24318202e-03 -1.14177787e+00 -1.28625298e+00 -9.44243073e-01
9.62575898e-02 2.84034908e-01 2.77424514e-01 -2.23469753... | [8.259710311889648, 2.557185411453247] |
c0285b74-ade8-4349-bbed-e284658270dc | visual-prompting-for-adversarial-robustness | 2210.06284 | null | https://arxiv.org/abs/2210.06284v4 | https://arxiv.org/pdf/2210.06284v4.pdf | Visual Prompting for Adversarial Robustness | In this work, we leverage visual prompting (VP) to improve adversarial robustness of a fixed, pre-trained model at testing time. Compared to conventional adversarial defenses, VP allows us to design universal (i.e., data-agnostic) input prompting templates, which have plug-and-play capabilities at testing time to achie... | ['Sijia Liu', 'Pin-Yu Chen', 'Yuguang Yao', 'Peter Lorenz', 'Aochuan Chen'] | 2022-10-12 | null | null | null | null | ['adversarial-defense', 'visual-prompting'] | ['adversarial', 'computer-vision'] | [ 1.70193866e-01 -2.43287310e-01 -7.46950135e-02 -8.36358294e-02
-9.03780341e-01 -1.45892131e+00 6.79284632e-01 -1.29272521e-01
-5.09229004e-02 4.33997631e-01 -2.29409188e-01 -1.05262387e+00
3.63328978e-02 -7.29923785e-01 -8.03803504e-01 -7.01669872e-01
3.82808782e-02 8.28378424e-02 4.26659703e-01 -3.24994713... | [5.660415172576904, 7.88810920715332] |
4c5230b8-9f87-4cff-9d0b-881183bf8645 | sparse-modular-activation-for-efficient | 2306.11197 | null | https://arxiv.org/abs/2306.11197v2 | https://arxiv.org/pdf/2306.11197v2.pdf | Sparse Modular Activation for Efficient Sequence Modeling | Linear State Space Models (SSMs) have demonstrated strong performance in a variety of sequence modeling tasks due to their efficient encoding of the recurrent structure. However, in more comprehensive tasks like language modeling and machine translation, self-attention-based models still outperform SSMs. Hybrid models ... | ['ChengXiang Zhai', 'Chenguang Zhu', 'Yichong Xu', 'Shuohang Wang', 'Yang Liu', 'Liliang Ren'] | 2023-06-19 | null | null | null | null | ['machine-translation', 'chunking'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.66833001e-01 2.12365463e-01 -6.49868131e-01 -2.35836789e-01
-5.69799244e-01 -2.87644416e-01 6.04242563e-01 -1.63671046e-01
-3.20669085e-01 5.33902705e-01 3.46060723e-01 -4.36120600e-01
3.13125134e-01 -4.95681316e-01 -8.90660584e-01 -6.10809147e-01
-1.26567036e-01 4.29796159e-01 1.52314991e-01 -3.05469781... | [10.810785293579102, 6.860292911529541] |
d8069253-36ee-4a89-9653-7ab340f06b91 | a-simple-yet-effective-baseline-for-3d-human | 1705.03098 | null | http://arxiv.org/abs/1705.03098v2 | http://arxiv.org/pdf/1705.03098v2.pdf | A simple yet effective baseline for 3d human pose estimation | Following the success of deep convolutional networks, state-of-the-art
methods for 3d human pose estimation have focused on deep end-to-end systems
that predict 3d joint locations given raw image pixels. Despite their excellent
performance, it is often not easy to understand whether their remaining error
stems from a l... | ['James J. Little', 'Rayat Hossain', 'Javier Romero', 'Julieta Martinez'] | 2017-05-08 | a-simple-yet-effective-baseline-for-3d-human-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Martinez_A_Simple_yet_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Martinez_A_Simple_yet_ICCV_2017_paper.pdf | iccv-2017-10 | ['monocular-3d-human-pose-estimation'] | ['computer-vision'] | [-2.09278435e-01 1.64187819e-01 -1.73201058e-02 -4.21299756e-01
-6.83795273e-01 -3.80895734e-01 3.92831892e-01 -2.56583959e-01
-6.75700188e-01 3.64425510e-01 3.70362967e-01 -1.05613306e-01
4.93327677e-01 -2.07149267e-01 -1.01760519e+00 5.44244871e-02
-2.72123426e-01 9.31338310e-01 1.51088983e-01 -4.48367208... | [6.95695686340332, -0.9592401385307312] |
1b458446-f0be-47de-8d9a-83422f0097de | convolutional-ensembling-based-few-shot | 2208.03288 | null | https://arxiv.org/abs/2208.03288v3 | https://arxiv.org/pdf/2208.03288v3.pdf | Convolutional Ensembling based Few-Shot Defect Detection Technique | Over the past few years, there has been a significant improvement in the domain of few-shot learning. This learning paradigm has shown promising results for the challenging problem of anomaly detection, where the general task is to deal with heavy class imbalance. Our paper presents a new approach to few-shot classific... | ['Sumeet Saurav', 'Prashant Sadashiv Gidde', 'Sanjay Singh', 'Abeer Banerjee', 'Soumyajit Karmakar'] | 2022-08-05 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 3.51055153e-02 -1.99074730e-01 -1.67168006e-01 -3.83939445e-01
-7.55105436e-01 1.30371392e-01 4.81297314e-01 5.32276094e-01
-4.35886294e-01 4.16855037e-01 -9.30994749e-02 2.02623345e-02
-1.32443413e-01 -9.09949303e-01 -4.67759609e-01 -5.85860372e-01
-1.72336683e-01 4.80949938e-01 6.06511354e-01 -5.35704792... | [9.862780570983887, 3.154982328414917] |
a0cc4816-e8f8-4cd7-9000-4a6c4b134c40 | prediction-of-oral-food-challenges-via | 2208.08268 | null | https://arxiv.org/abs/2208.08268v2 | https://arxiv.org/pdf/2208.08268v2.pdf | Prediction of Oral Food Challenge Outcomes via Ensemble Learning | Oral Food Challenges (OFCs) are essential to accurately diagnosing food allergy due to the limitations of existing clinical testing. However, some patients are hesitant to undergo OFCs, while those willing suffer from limited access to allergists in rural/community healthcare settings. Despite its success in predicting... | ['Jonathan Gryak', 'Georgiana Sanders', 'Rajan Ravikumar', 'Kayvan Najarian', 'Diane Shaltis', 'Kylie Jungles', 'Deborah Lee', 'Justin Zhang'] | 2022-08-17 | null | null | null | null | ['predicting-patient-outcomes'] | ['medical'] | [ 4.61120605e-02 -4.13649589e-01 -4.84580278e-01 -3.35065484e-01
-7.91701317e-01 -7.90673912e-01 -2.88119227e-01 1.22359741e+00
-2.50069976e-01 2.39646286e-01 -1.06454529e-01 -7.95802534e-01
-5.57985544e-01 -7.44448185e-01 -6.46245897e-01 -6.05084479e-01
-4.12533373e-01 3.94430190e-01 -4.39691208e-02 -3.20096090... | [8.187427520751953, 5.582147121429443] |
6288ec01-9c17-4714-9374-3b052133adbd | learning-by-novel-view-synthesis-for-full | 2201.07927 | null | https://arxiv.org/abs/2201.07927v3 | https://arxiv.org/pdf/2201.07927v3.pdf | Learning-by-Novel-View-Synthesis for Full-Face Appearance-Based 3D Gaze Estimation | Despite recent advances in appearance-based gaze estimation techniques, the need for training data that covers the target head pose and gaze distribution remains a crucial challenge for practical deployment. This work examines a novel approach for synthesizing gaze estimation training data based on monocular 3D face re... | ['Yusuke Sugano', 'Takuru Shimoyama', 'Jiawei Qin'] | 2022-01-20 | null | null | null | null | ['3d-face-reconstruction', 'gaze-estimation', 'face-reconstruction'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.13563263e-01 1.27918318e-01 -2.81098615e-02 -7.70941734e-01
-6.77524924e-01 -3.91336232e-01 3.44135851e-01 -9.94443417e-01
1.34829596e-01 5.94179034e-01 -8.05506632e-02 4.81097363e-02
1.02462053e-01 -9.67051089e-02 -7.90762305e-01 -5.23737073e-01
3.79909575e-01 3.22680622e-01 -1.40081614e-01 1.32256746... | [14.050582885742188, 0.03887830302119255] |
bbacc723-f1ee-4fc5-84a8-b5e3883f29bb | phd-gifs-personalized-highlight-detection-for | 1804.06604 | null | http://arxiv.org/abs/1804.06604v2 | http://arxiv.org/pdf/1804.06604v2.pdf | PHD-GIFs: Personalized Highlight Detection for Automatic GIF Creation | Highlight detection models are typically trained to identify cues that make
visual content appealing or interesting for the general public, with the
objective of reducing a video to such moments. However, the "interestingness"
of a video segment or image is subjective. Thus, such highlight models provide
results of lim... | ['Ana García del Molino', 'Michael Gygli'] | 2018-04-18 | null | null | null | null | ['highlight-detection'] | ['computer-vision'] | [ 1.77625995e-02 -2.35335022e-01 -5.22832811e-01 -4.10286427e-01
-8.87683809e-01 -6.47737801e-01 5.66598773e-01 3.01143765e-01
-4.41061080e-01 4.36367750e-01 4.91647720e-01 1.24795437e-01
3.59102994e-01 -5.02887249e-01 -7.15768337e-01 -2.36242071e-01
-1.41793102e-01 2.10057385e-03 5.94129860e-01 6.35702312... | [10.151287078857422, 0.5065709948539734] |
3e716d93-e7a3-4159-9cc4-bb88a58e349d | elixirnet-relation-aware-network-architecture | 2003.08770 | null | https://arxiv.org/abs/2003.08770v1 | https://arxiv.org/pdf/2003.08770v1.pdf | ElixirNet: Relation-aware Network Architecture Adaptation for Medical Lesion Detection | Most advances in medical lesion detection network are limited to subtle modification on the conventional detection network designed for natural images. However, there exists a vast domain gap between medical images and natural images where the medical image detection often suffers from several domain-specific challenge... | ['Xiaodan Liang', 'Shaoju Wang', 'Chenhan Jiang', 'Nong Xiao', 'Hang Xu'] | 2020-03-03 | null | null | null | null | ['medical-image-detection'] | ['computer-vision'] | [ 4.03246969e-01 3.26476008e-01 -6.29887223e-01 -2.36336350e-01
-6.59380674e-01 -3.60691488e-01 2.27519304e-01 3.08065355e-01
-2.35542864e-01 5.22207737e-01 2.33147770e-01 -3.69076043e-01
-3.78914922e-01 -8.71295869e-01 -1.82872087e-01 -7.11006641e-01
-6.31028488e-02 2.89328516e-01 9.88172352e-01 -3.58750075... | [15.13215446472168, -2.4620988368988037] |
b5bec304-7875-477e-9c09-cf0d4b416780 | permutation-invariant-variational-autoencoder | 2104.09856 | null | https://arxiv.org/abs/2104.09856v2 | https://arxiv.org/pdf/2104.09856v2.pdf | Permutation-Invariant Variational Autoencoder for Graph-Level Representation Learning | Recently, there has been great success in applying deep neural networks on graph structured data. Most work, however, focuses on either node- or graph-level supervised learning, such as node, link or graph classification or node-level unsupervised learning (e.g. node clustering). Despite its wide range of possible appl... | ['Djork-Arné Clevert', 'Frank Noé', 'Robin Winter'] | 2021-04-20 | null | http://proceedings.neurips.cc/paper/2021/hash/4f3d7d38d24b740c95da2b03dc3a2333-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/4f3d7d38d24b740c95da2b03dc3a2333-Paper.pdf | neurips-2021-12 | ['graph-reconstruction'] | ['graphs'] | [ 3.72867286e-01 6.73023522e-01 -2.75333017e-01 -2.73572654e-01
-2.21920356e-01 -3.86532098e-01 6.99930847e-01 5.93849003e-01
-1.03731103e-01 4.25950825e-01 1.59500584e-01 -3.96380216e-01
-2.47464627e-01 -1.19489384e+00 -7.27473915e-01 -7.31224358e-01
-4.18014765e-01 5.89271724e-01 -1.58719972e-01 1.02330185... | [7.098913192749023, 6.19667387008667] |
bd114b13-0656-4fe7-97ac-6cc686c35c99 | 190511559 | 1905.11559 | null | https://arxiv.org/abs/1905.11559v1 | https://arxiv.org/pdf/1905.11559v1.pdf | Road Segmentation with Image-LiDAR Data Fusion | Robust road segmentation is a key challenge in self-driving research. Though many image-based methods have been studied and high performances in dataset evaluations have been reported, developing robust and reliable road segmentation is still a major challenge. Data fusion across different sensors to improve the perfor... | ['Zeren Sun', 'Yazhou Yao', 'Xiangrui Li', 'Huafeng Liu', 'Zhenmin Tang', 'Ke Jia'] | 2019-05-26 | null | null | null | null | ['road-segementation'] | ['computer-vision'] | [ 4.01773632e-01 -1.46076411e-01 1.63378686e-01 -7.12180257e-01
-7.05870926e-01 -2.96546578e-01 4.00936633e-01 4.05947585e-03
-6.49783373e-01 7.62264788e-01 -1.87095523e-01 -1.91516325e-01
-7.58423731e-02 -1.20921314e+00 -9.91437018e-01 -3.24580908e-01
3.80358934e-01 4.28973615e-01 8.78277838e-01 -3.02330375... | [8.749314308166504, -1.5683765411376953] |
1c09524a-33cf-482b-a4ae-8f60270ec1c4 | deep-neural-network-for-semantic-based-text | 1908.01403 | null | https://arxiv.org/abs/1908.01403v3 | https://arxiv.org/pdf/1908.01403v3.pdf | Deep Neural Network for Semantic-based Text Recognition in Images | State-of-the-art text spotting systems typically aim to detect isolated words or word-by-word text in images of natural scenes and ignore the semantic coherence within a region of text. However, when interpreted together, seemingly isolated words may be easier to recognize. On this basis, we propose a novel "semantic-b... | ['Qitong Wang', 'Yi Zheng', 'Margrit Betke'] | 2019-08-04 | null | null | null | null | ['text-spotting'] | ['computer-vision'] | [ 5.90622962e-01 -1.91764995e-01 -3.35999243e-02 -5.68886876e-01
-7.09117532e-01 -6.91253126e-01 7.53288746e-01 5.75982012e-05
-3.66391867e-01 1.33924544e-01 2.30567932e-01 -2.72450268e-01
1.91416174e-01 -3.69920135e-01 -7.55219221e-01 -4.01859611e-01
7.04874575e-01 7.34353423e-01 3.25056344e-01 -1.19236685... | [11.818435668945312, 2.2780613899230957] |
1e62d0e5-1062-433c-9b82-1105e86fb9bd | teaching-small-language-models-to-reason | 2212.08410 | null | https://arxiv.org/abs/2212.08410v3 | https://arxiv.org/pdf/2212.08410v3.pdf | Teaching Small Language Models to Reason | Chain of thought prompting successfully improves the reasoning capabilities of large language models, achieving state of the art results on a range of datasets. However, these reasoning capabilities only appear to emerge in models with a size of over 100 billion parameters. In this paper, we explore the transfer of suc... | ['Aliaksei Severyn', 'Eric Malmi', 'Jakub Adamek', 'Jonathan Mallinson', 'Lucie Charlotte Magister'] | 2022-12-16 | null | null | null | null | ['gsm8k'] | ['natural-language-processing'] | [ 1.67403948e-02 6.21010244e-01 8.64755288e-02 -1.94209054e-01
-4.64504242e-01 -8.01147282e-01 8.60065937e-01 2.34366983e-01
-3.03853720e-01 6.41971171e-01 2.75817126e-01 -9.85168874e-01
-4.50081080e-01 -1.06131279e+00 -9.42491412e-01 -2.32579529e-01
1.44174531e-01 7.19351947e-01 1.29996628e-01 -4.91740018... | [9.672781944274902, 7.354310989379883] |
41e6eaac-9601-44cd-9521-63b76213d42b | progressive-multi-task-learning-framework-for | 2306.17447 | null | https://arxiv.org/abs/2306.17447v2 | https://arxiv.org/pdf/2306.17447v2.pdf | Progressive Multi-task Learning Framework for Chinese Text Error Correction | Chinese Text Error Correction (CTEC) aims to detect and correct errors in the input text, which benefits human's daily life and various downstream tasks. Recent approaches mainly employ Pre-trained Language Models (PLMs) to resolve CTEC task and achieve tremendous success. However, previous approaches suffer from issue... | ['Ying Shen', 'Hai-Tao Zheng', 'Yangning Li', 'Shulin Huang', 'Haojing Huang', 'Yinghui Li', 'Shirong Ma'] | 2023-06-30 | null | null | null | null | ['multi-task-learning'] | ['methodology'] | [ 3.89824450e-01 -3.62063676e-01 1.42296880e-01 -2.91233152e-01
-1.11711204e+00 -4.50241640e-02 3.38723302e-01 3.96155089e-01
-7.39334047e-01 8.71348202e-01 2.41789892e-01 -4.49377358e-01
1.40912279e-01 -3.84523362e-01 -6.61976099e-01 -2.36287430e-01
6.32923603e-01 2.77393013e-01 3.08047026e-01 -4.94135767... | [10.988786697387695, 10.825719833374023] |
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